https://aistudio.google.com/app/prompts?state=%7B%22ids%22:%5B%221PZ6r7VKo88WU2PP3JnhsPdz45sk00iGI%22%5D,%22action%22:%22open%22,%22userId%22:%22108467125666360745177%22,%22resourceKeys%22:%7B%7D%7D&usp=sharing #!/bin/bash set -e -E GEMINI_API_KEY="$GEMINI_API_KEY" MODEL_ID="gemini-2.5-pro-preview-03-25" GENERATE_CONTENT_API="streamGenerateContent" cat << EOF > request.json { "contents": [ { "role": "user", "parts": [ { "text": "gemini, help with a small idea. thinking of a text adventure game where the ai performs function calling to update notes on rooms and the world that describe what is going on. so, the backend would hold a graph of rooms which were these collections of notes, and the ai would have functions to update them in response to user behaviors. the prompts would have to precisely and firmly tell the ai not to add _any_ details that were not mentioned in the notes when communicating with the user, unless _every_ new detail was updated in the notes, so as to maintain strong consistency.\nany thoughts?" }, ] }, { "role": "model", "parts": [ { "text": "Here's a thinking process that could lead to the detailed response about the AI-powered text adventure game:\n\n1. **Deconstruct the User's Core Idea:**\n * **Goal:** Create a text adventure game.\n * **Key Mechanism:** AI uses function calling to update game state (notes on rooms/world).\n * **Game State Representation:** A graph of rooms, where each room is a collection of notes describing its state.\n * **AI Role:**\n * Interprets user input.\n * Decides which functions to call based on input and current state.\n * Calls functions to update the notes (game state).\n * Generates descriptive text for the user *based solely on the updated notes*.\n * **Crucial Constraint:** AI must *not* hallucinate details. Any detail presented to the user must either be pre-existing in the notes or *just added* to the notes via function call. Strict consistency is paramount.\n\n2. **Identify Strengths of the Idea:**\n * **Dynamic World:** Function calling allows the world state to change meaningfully based on player actions, going beyond simple pre-scripted responses.\n * **Consistency:** The \"notes-first\" approach directly addresses the common AI hallucination problem in creative generation. By forcing the AI to ground its output in the explicit state, consistency is enforced.\n * **Scalability (Potentially):** A graph structure is good for representing interconnected spaces. The note-based system could potentially handle complex states within rooms.\n * **Emergent Gameplay:** Interactions between different notes/states updated by functions could lead to unexpected but logical outcomes.\n * **Leverages Modern AI:** Uses function calling, a key capability of current LLMs.\n\n3. **Identify Potential Challenges & Weaknesses:**\n * **Prompt Engineering:** Getting the AI to *strictly* adhere to the \"notes-only\" rule will be difficult. It requires very precise, firm, and potentially complex prompting. How do you handle nuances, implied actions, or sensory details not explicitly noted?\n * **Function Design:** Defining the right set of functions is critical. Too few, and the game is limited. Too many, and it becomes complex to manage and for the AI to choose correctly. What granularity should the functions have? (e.g., `update_item_location`, `change_room_description`, `set_npc_mood`?)\n * **State Representation (Notes):** How are the notes structured? Plain text? Key-value pairs? JSON? This affects how easily the AI can parse them and how effectively functions can update them. How do you handle relationships *between* notes?\n * **Performance/Latency:** Each turn involves: User Input -> AI Processing (Interpretation + Function Choice) -> Function Execution (Backend Update) -> AI Processing (Text Generation based on new state) -> User Output. This could be slow.\n * **Cost:** LLM API calls (especially with function calling and potentially multiple steps per turn) can be expensive.\n * **\"Dryness\" of Output:** If the AI *only* reports what's in the notes, the descriptions might become very factual and lack evocative language or atmosphere unless the *notes themselves* are written poetically, or the AI is specifically prompted to embellish *based only on the notes*. How do you balance strictness with engaging prose?\n * **Error Handling:** What happens if the AI calls the wrong function? Or hallucinates a function call? Or fails to update the notes correctly before generating text?\n * **Initial World Building:** Creating the initial graph and notes requires significant upfront effort.\n\n4. **Brainstorm Solutions and Refinements:**\n * **Prompting:** Use strong negative constraints (\"DO NOT mention anything not present in the provided notes unless it was just added by a function call you made this turn\"). Use few-shot examples in the prompt showing correct and incorrect behavior. Consider a multi-step process: 1) Interpret input & decide actions/updates. 2) Call functions. 3) Receive updated notes. 4) Generate description *only* from these notes.\n * **Function Design:** Start simple. Core functions: `move(direction)`, `examine(object/feature)`, `take(item)`, `use(item, target)`, `update_description(room_id, new_text)`, `add_note(room_id, key, value)`, `remove_note(room_id, key)`. Gradually add more complex ones. Ensure functions return success/failure and potentially the updated state snippet.\n * **State Representation:** Structured data (like JSON within the notes or a dedicated database) is likely better than free-form text for reliable updates. Maybe a mix: a main descriptive text note, plus key-value pairs for specific states (e.g., `light_level: dim`, `door_north: locked`, `item_key: on_table`).\n * **Performance:** Optimize function calls. Maybe batch updates. Use smaller/faster models if possible for certain steps. Cache room states.\n * **Cost:** Monitor usage. Implement limits. Consider local models if feasible, though function calling might be less robust.\n * **Output Quality:** Allow the AI *some* leeway to rephrase or connect information *from the notes*, but not invent new *facts*. Prompt it to be descriptive *using only the provided details*. Maybe have separate \"flavor text\" notes that can be included. Or, the functions themselves could update descriptive snippets.\n * **Error Handling:** Implement validation on the backend for function calls. Have the AI self-correct if it generates text inconsistent with the notes (though this is hard). Log errors meticulously.\n * **World Building:** Develop tools to help create the initial graph and notes. Start with a very small, manageable world.\n\n5. **Structure the Response:**\n * **Acknowledge and Validate:** Start by saying it's a cool and interesting idea. Mention the core strengths.\n * **Elaborate on the Core Concept:** Briefly explain *how* it would work step-by-step (User Input -> AI -> Function Call -> Backend Update -> AI -> User Output).\n * **Discuss Strengths in Detail:** Expand on the points identified in step 2.\n * **Discuss Challenges in Detail:** Expand on the points identified in step 3. This is crucial for a realistic assessment.\n * **Suggest Solutions and Considerations:** Offer concrete ideas based on step 4 to address the challenges. Organize these logically (Prompting, Functions, State, etc.).\n * **Propose a Minimal Viable Product (MVP):** Suggest starting small to test the core mechanics.\n * **Concluding Thought:** Reiterate the potential and the challenge, ending on an encouraging note.\n\n6. **Refine Language:** Use clear, concise language. Ensure the tone is helpful and constructive. Use formatting (bolding, bullet points) to improve readability. Double-check that all aspects of the user's original prompt have been addressed. Ensure the \"strict consistency\" constraint is emphasized throughout." }, { "text": "Okay, this is a really interesting and potentially powerful idea for a text adventure! Leveraging AI function calling for state management is clever. Let's break down the pros, cons, and considerations:\n\n**Strengths of this Approach:**\n\n1. **Dynamic & Reactive World:** This is the biggest win. Instead of pre-scripted outcomes, the AI can interpret nuanced player actions and modify the world state in potentially complex ways via function calls. This could lead to more emergent gameplay.\n2. **Consistency Enforcement:** Your core constraint – forcing the AI to ground *all* descriptions in the explicit notes (either pre-existing or just updated) – directly tackles the AI hallucination problem. This is crucial for a coherent game world.\n3. **Separation of Concerns:** The AI handles natural language understanding and response generation, while the backend handles the \"truth\" of the game state (the notes/graph). This is a good architectural pattern.\n4. **Leverages Modern AI Capabilities:** Function calling is a key strength of models like Gemini, making this feasible in a way that wasn't practical before.\n5. **Potential for Complexity:** The \"notes\" system is flexible. Notes could represent object locations, NPC moods, environmental states (light level, temperature), puzzle statuses, etc.\n\n**Potential Challenges & Considerations:**\n\n1. **Prompt Engineering is Critical (and Hard):**\n * **Strictness:** You need *extremely* precise and firm instructions in the system prompt. Emphasize repeatedly that *no* detail, however small or seemingly obvious, should be mentioned unless it's explicitly in the provided notes for the current context (room, inventory, etc.) or was just added via a function call *in the current turn*.\n * **Handling Implication:** How does the AI handle things that are *implied* but not stated? If notes say \"There is a lit torch,\" can the AI say \"The room is brightly lit\"? You need to define the boundary. Perhaps the function call `add_note(room_id, 'light_level', 'bright')` must happen alongside placing the torch.\n * **Descriptive Flair vs. Factual Reporting:** If the AI *only* reports facts from notes, descriptions might become dry. \"There is a table. There is a key on the table. The door is north.\" You might need to prompt the AI to use the notes as a basis for *more evocative* descriptions, but without adding *new factual information*. This is a delicate balance.\n\n2. **Function Design:**\n * **Granularity:** What level of detail do functions operate on? `update_room_description(room_id, new_description)`? Or more granular like `move_item(item_id, source_container_id, dest_container_id)`, `set_property(object_id, property_name, value)`? Granular is more flexible but requires more complex AI logic and potentially more function calls per turn.\n * **Atomicity:** What if an action requires multiple state changes? Should the AI make multiple function calls? What if one fails? Does the backend need to handle transactions?\n * **Discovery:** How does the AI know *what* functions are available and what they do? This is standard function calling setup, but needs careful definition.\n\n3. **State Representation (The Notes):**\n * **Format:** Plain text? Key-value pairs? JSON objects? Structured data (like JSON) within the notes will be much easier for functions to parse and update reliably than free-form text.\n * **Scope:** Are notes just per-room? What about global world state? Player inventory? NPC state? The \"graph of rooms with notes\" needs to encompass the entire game state accessible to the player.\n * **Initialization:** Defining the initial state of all notes for all rooms requires significant setup.\n\n4. **Performance and Cost:**\n * Each player turn might involve:\n 1. AI call to understand input, consult current notes, and decide function calls.\n 2. Backend execution of function calls (updating the notes database/graph).\n 3. AI call (potentially) to generate the response based on the *updated* notes.\n * This could lead to noticeable latency and higher API costs compared to simpler approaches.\n\n5. **Error Handling:**\n * What if the AI hallucinates a function call or parameters?\n * What if the AI generates text inconsistent with the notes *despite* the prompt? (This is the core challenge). You might need a validation step comparing the generated output against the notes.\n * What if a function call fails on the backend? How is that reported back to the AI/player?\n\n**How to Mitigate Challenges / Implementation Ideas:**\n\n* **Structured Notes:** Use JSON or a similar structured format for notes within each room/object. This makes function targeting precise (e.g., `update_note(room_id='CaveEntrance', note_key='description', new_value='A dark cave entrance looms.')`, `set_property(item_id='key_01', property='location', value='player_inventory')`).\n* **Two-Step AI Process:**\n 1. **Planning Step:** Give the AI the user input and current relevant notes. Ask it to determine the necessary state changes and output *only* the function calls required.\n 2. **Execution Step:** Execute those function calls on your backend.\n 3. **Generation Step:** Give the AI the *updated* notes (and maybe a summary of actions taken) and instruct it *strictly* to describe the current situation based *only* on these notes.\n* **Start Simple (MVP):**\n * A few rooms.\n * Basic functions: `move(direction)`, `examine(object)`, `take(item)`, `update_note(target, key, value)`.\n * Simple key-value notes.\n * Focus intensely on nailing the prompt for consistency.\n* **Explicit Feedback Loop:** If possible, have the backend validate the AI's *final output* against the notes before sending it to the user. If inconsistent, potentially ask the AI to regenerate with stronger constraints or log an error.\n\n**Conclusion:**\n\nThis is a promising and innovative approach! It directly addresses the consistency problem inherent in using LLMs for stateful games. The main hurdles will be the robustness of the prompt engineering to enforce the \"notes-only\" rule and the design of a flexible yet manageable function/notes system. If you can overcome those, you could create a uniquely dynamic and coherent text adventure experience. Start small, iterate, and focus heavily on that core consistency mechanism. Good luck!" }, ] }, { "role": "user", "parts": [ { "text": "hrm" }, ] }, { "role": "model", "parts": [ { "text": "Thinking Process:\n\n1. **Analyze the User's Input:** The user responded with \"hrm\". This is a non-committal, thoughtful sound. It suggests they are processing the previous information, perhaps seeing the complexities or potential roadblocks I outlined. It's not a question, nor a strong agreement or disagreement. It's an invitation for further thought or clarification, or perhaps just an acknowledgement of the points raised.\n\n2. **Identify the Goal:** The goal is to be helpful and continue the conversation productively. Since the user hasn't given specific direction, I need to offer avenues for them to explore based on the previous discussion.\n\n3. **Recall the Previous Context:** The core idea was an AI text adventure using function calls to update notes (game state) and strictly grounding AI descriptions in those notes. I outlined strengths, challenges (prompting, function design, state representation, performance, error handling), and mitigation strategies.\n\n4. **Brainstorm Potential User Thoughts behind \"hrm\":**\n * \"Okay, that sounds more complicated than I initially thought.\"\n * \"The prompt engineering part seems particularly tricky.\"\n * \"How would I actually implement the structured notes or the two-step AI process?\"\n * \"Is this *really* better than traditional methods?\"\n * \"Maybe I should simplify the idea.\"\n * \"Just acknowledging the points, thinking...\"\n\n5. **Formulate Helpful Responses based on Potential Thoughts:**\n * **Acknowledge the complexity:** Validate that it's a non-trivial problem. \"It definitely involves some tricky parts...\"\n * **Offer to elaborate:** Ask if any specific point stood out or caused the \"hrm\". \"Was there a particular aspect that sparked that thought? The prompt engineering? The function design?\"\n * **Suggest focusing:** Propose breaking it down. \"Perhaps we could focus on one piece, like how the notes might be structured?\"\n * **Reiterate the core value:** Briefly remind them *why* this approach is interesting despite the challenges. \"...but the potential for a truly dynamic and consistent world is compelling.\"\n * **Open the floor:** Ask a general question to encourage them to share their specific thoughts. \"What's running through your mind?\" or \"Any specific concerns bubbling up?\"\n\n6. **Synthesize and Refine:** Combine these elements into a concise and helpful response. Start by acknowledging the thoughtful pause. Then, offer specific areas from the previous discussion as potential points of focus. End with an open invitation for them to direct the conversation. Avoid making assumptions about *why* they said \"hrm\".\n\n7. **Self-Correction/Refinement:** Initial thought: Just ask \"What are you thinking?\". Better: Acknowledge the pause and offer specific hooks related to the prior conversation to make it easier for the user to respond. Ensure the tone is collaborative and supportive." }, { "text": "\"Hrm\" indeed! It signals you're mulling over the complexities and possibilities.\n\nDoes that \"hrm\" lean towards:\n\n* **\"Hrm, that sounds more complicated than I first imagined...\"** (Especially the prompt engineering or function design?)\n* **\"Hrm, I see the potential, but also the pitfalls...\"** (Concerned about consistency actually working, or performance?)\n* **\"Hrm, how would I actually *start* building this...\"** (Thinking about the practical steps, like note structure or initial functions?)\n* **\"Hrm, maybe there's a simpler way to achieve a similar goal...\"** (Considering alternatives?)\n* Or just a general **\"Hrm, lots to think about.\"**?\n\nKnowing what aspect sparked the \"hrm\" might help focus the next step of brainstorming or problem-solving. What's on your mind?" }, ] }, { "role": "user", "parts": [ { "text": "let's prioritize keeping it simple and making it succeed reliably.\ni was thinking the ai could generate the rooms using a function call.\ngemini has an existing interface for user-defined function calls, as do other major models nowadays" }, ] }, { "role": "model", "parts": [ { "text": "Here's a thinking process for responding to the user's desire for simplicity and reliability, focusing on AI-generated rooms via function calls:\n\n1. **Acknowledge and Validate:** The user wants simplicity and reliability. This is a good starting point. Acknowledge this priority (\"Excellent goal. Simplicity and reliability are key...\"). Also, validate the idea of AI generating rooms via function calls (\"...and using function calls for room generation fits well with leveraging the AI's strengths.\").\n\n2. **Connect to Previous Discussion:** How does this new idea (AI generating rooms) fit into the original concept (AI updating notes based on player actions)?\n * It's a *different* use of function calls. The original was about *runtime state updates*. This is about *content generation*.\n * It can *complement* the original idea. The AI generates the initial room notes, and *then* the runtime system (AI + functions) updates those notes.\n * It introduces its *own* set of simplicity/reliability challenges.\n\n3. **Analyze the \"AI Generates Rooms\" Idea (Pros & Cons focused on Simplicity/Reliability):**\n\n * **Pros (Simplicity/Reliability Focus):**\n * **Leverages AI Creativity:** Good for generating varied content without manual effort.\n * **Structured Output:** Function calling *forces* the AI to output structured data (the room notes/description), which is inherently more reliable for the backend than parsing free text.\n * **Potentially Faster Setup:** Might be quicker to get *some* content than hand-crafting everything initially.\n\n * **Cons (Simplicity/Reliability Focus):**\n * **Consistency *Between* Rooms:** How do you ensure the AI generates rooms that logically connect? (e.g., a door north in Room A should lead to Room B which has a door south). This requires careful prompting or a multi-stage generation process. *This is a major reliability concern.*\n * **Controllability:** Getting the AI to generate *exactly* the kind of room you want (specific theme, items, puzzle elements) can be tricky. Requires detailed prompting for the generation function. Less simple than just writing it yourself if you have a specific vision.\n * **Initial State Complexity:** The generated notes still need to be comprehensive enough for the *runtime* AI to work with later. If the generation is too sparse, the runtime descriptions will be poor.\n * **Debugging:** If the AI generates a nonsensical room or connection, debugging *why* can be harder than debugging hand-written content.\n\n4. **Synthesize and Propose a Simple, Reliable Workflow:** How can we combine the ideas while maximizing simplicity and reliability?\n\n * **Initial World Generation:**\n * **Option A (Simpler, More Reliable):** *Manually* define the core room structure (graph connections) and maybe key items/plot points. Then, use an AI function call (`generate_room_details(room_id, theme, required_items)`) to flesh out the *descriptions and minor details* based on your structure. This gives control *and* leverages AI creativity.\n * **Option B (More AI-driven, Less Reliable Initially):** Use AI to generate the *entire* structure and details. This requires a more complex generation function (`generate_connected_room(previous_room_id, exit_direction, theme_constraints)`) and likely post-generation validation/cleanup by the developer. Less simple to get right initially.\n\n * **Runtime Gameplay (Original Idea):**\n * Use the AI + function calls (`update_note`, `move_item`, etc.) to modify the state (notes) based on player actions.\n * Strict prompting remains crucial here to ensure the AI only describes what's in the *current* notes.\n\n5. **Refine the \"Generate Room\" Function Call:** What would a simple, reliable function look like?\n * `create_room(room_id, name, description, items=[], exits={})` - This is very basic. The AI would call this.\n * Better: `generate_room_data(theme, connections_needed={'north': 'existing_room_id', 'west': None}, required_items=['key'])` - The AI is prompted to generate details fitting the theme and constraints, then calls a backend function `add_room_to_graph(room_data)` with the structured output.\n\n6. **Structure the Response:**\n * Start by agreeing with the user's priority (simplicity, reliability).\n * Explain how AI room generation fits in.\n * Discuss the pros/cons *specifically* related to simplicity and reliability.\n * Propose a concrete, simplified workflow (Manual structure + AI details seems best for reliability).\n * Briefly touch on the function call structure for generation.\n * Reiterate that the *runtime* consistency challenge (AI describing only from notes) remains separate but equally important.\n * End with an open question to guide the next step.\n\n7. **Self-Correction/Refinement:** Is the proposed workflow *actually* simpler? Yes, compared to full AI generation or complex manual scripting. Is it *reliable*? More reliable than full AI generation because the core structure is controlled. Does it address the user's points? Yes, it incorporates AI generation via function calls while prioritizing reliability. Ensure the distinction between *generation* functions and *runtime update* functions is clear." }, { "text": "Okay, prioritizing simplicity and reliability is smart, especially when starting out. Using AI function calls for *generating* the initial room data is definitely feasible and plays to the AI's strengths.\n\nLet's refine the idea with that focus:\n\n**Simplified Workflow:**\n\n1. **Developer Defines Core Structure (Optional but Recommended for Reliability):** You might still want to define the basic map layout (e.g., \"Room A connects North to Room B\") manually. This gives you control over the game's flow and prevents the AI from generating nonsensical geography.\n2. **AI Generates Room Details via Function Call:**\n * You prompt the AI: \"Generate the details for a room described as 'a dusty library'. It needs an exit to the north (leading to 'Room B') and should contain 'an old book' and 'a broken lantern'.\"\n * The AI understands this and decides to call a function you've defined, maybe `create_room_data`.\n * **Function Call:** The AI calls `create_room_data(name='Dusty Library', description_prompt='A dusty library, quiet and forgotten', exits={'north': 'Room B'}, required_items=['old book', 'broken lantern'])`.\n * **Backend Function (`create_room_data`):** This function *could* internally use the AI again to generate a more evocative description based on the prompt, or it could use simpler template-based generation. Crucially, it structures the output reliably.\n * **Output:** The function returns structured data (e.g., JSON) representing the room's initial state:\n ```json\n {\n \"room_id\": \"room_A\", // Or generated UUID\n \"name\": \"Dusty Library\",\n \"notes\": {\n \"description\": \"Sunlight streams through a grimy window, illuminating swirling dust motes. Bookshelves line the walls, many empty, others crammed with decaying volumes. The air smells of old paper and neglect.\",\n \"exits\": { \"north\": \"Room B\" },\n \"items_present\": [\"book_01\", \"lantern_01\"],\n \"ambient_light\": \"dim\"\n },\n \"items\": {\n \"book_01\": { \"name\": \"old book\", \"description\": \"A heavy, leather-bound tome. Its cover is featureless.\", \"location\": \"room_A\" },\n \"lantern_01\": { \"name\": \"broken lantern\", \"description\": \"A rusty metal lantern with cracked glass.\", \"location\": \"room_A\", \"is_broken\": true }\n }\n }\n ```\n * **Backend Storage:** Your backend takes this structured data and stores it (in your graph/database).\n\n3. **Runtime Gameplay (Original Idea - Still Crucial):**\n * When the player enters the \"Dusty Library\", you provide the AI with the `notes` section for `room_A`.\n * **The Strict Prompt:** \"You are in the 'Dusty Library'. Describe the scene *using only information explicitly present in these notes*. Do not add any details not found here. Notes: `[Insert notes JSON or relevant parts here]`.\"\n * Player interacts: \"examine book\"\n * AI gets input + relevant notes (room notes + book notes).\n * AI determines action: Needs to describe the book.\n * AI generates response based *only* on the provided notes for `book_01`.\n * Player interacts: \"take book\"\n * AI determines action: Move the book.\n * **Function Call:** AI calls `update_item_location(item_id='book_01', new_location='player_inventory')`.\n * **Backend:** Updates the state for `book_01` and potentially removes it from `room_A`'s `items_present` list.\n * **AI Response Generation:** AI gets the *updated* notes and describes the result: \"You take the old book.\"\n\n**Advantages of this Refined Approach:**\n\n* **Simplicity in Generation:** You offload the creative writing of descriptions and item placement (within constraints) to the AI.\n* **Reliability via Structure:** The function call forces the AI's generation into a structured format your backend can easily understand and store. This is much more reliable than asking the AI to just write free-text descriptions you then have to parse.\n* **Clear Separation:** Generation (using `create_room_data`) is separate from runtime updates (using `update_item_location`, `add_note`, etc.).\n* **Leverages AI Strengths:** Uses AI for generation (where creativity is good) and structured interaction (function calls).\n* **Maintains Runtime Consistency:** The core challenge of ensuring the AI *only* describes the current state based on the notes during gameplay remains, but the initial state itself is reliably generated and stored.\n\n**Key Considerations for Simplicity/Reliability:**\n\n* **Generation Function Design:** Keep the `create_room_data` function (and the prompts that trigger it) focused. Start simple – maybe just generate description, items, and confirm exits. Add complexity later.\n* **Backend State Management:** Your backend needs to reliably store and retrieve this structured data (the notes). A simple dictionary or JSON store per room might be enough initially.\n* **The Runtime Prompt:** This is still the absolute linchpin for reliable gameplay consistency. It needs to be rigorously tested.\n\nThis approach seems like a solid path forward, balancing the power of AI generation with the need for a reliable, structured game state. Does this align better with your vision?" }, ] }, { "role": "user", "parts": [ { "text": "there can be a problem with this approach regarding game events ... the user can type things assuming the game master has knowledge of the entire flow. if it's not all kept in history, then strange things can happen unless nonintuitive notes are taken. it's simpler to keep the whole flow in history" }, ] }, { "role": "model", "parts": [ { "text": "Here's a thinking process for responding to the user's concern about conversation history vs. state notes:\n\n1. **Identify the Core Concern:** The user is pointing out a potential disconnect. The player *assumes* the AI (acting as Game Master) remembers the entire conversation history and understands the narrative flow. The proposed system relies heavily on discrete \"notes\" representing the *current state*. If an event's significance relies on *how* we got here (the history), simply looking at the current notes might not be enough, leading to AI responses that feel \"off\" or lack context.\n\n2. **Acknowledge the Validity:** The user's point is valid. LLMs *do* typically rely on conversation history for context. Relying *solely* on discrete state notes *could* lose narrative nuance or lead to the AI forgetting *why* something is the way it is. Example: Player convinces an NPC through a long dialogue. Notes might just say `npc_mood: friendly`. If the player later refers back to the *reason* the NPC is friendly (\"Remember when I helped you with X?\"), an AI looking *only* at the notes might not connect the dots.\n\n3. **Analyze the Proposed Solution (\"Keep the whole flow in history\"):**\n * **Pros:** Provides maximum context to the AI. The AI sees the entire player journey, dialogue, and actions. This is how standard chatbots work and how LLMs naturally maintain conversational flow. It directly addresses the user's concern.\n * **Cons:**\n * **Scalability/Token Limits:** Conversation histories can get very long, potentially exceeding the AI's context window limit, especially in a long game. This leads to truncation and loss of *early* history.\n * **Cost:** Processing long histories on every turn increases API costs.\n * **State Management Conflict:** How does the history interact with the function-called state updates? If the history says \"Player picked up the key\" but the notes (due to a bug or missed function call) still say the key is on the table, which does the AI trust? This reintroduces the consistency problem the notes system was designed to solve. The AI might hallucinate based on history even if the notes are correct.\n * **Complexity:** Managing *both* a potentially long history *and* a discrete state system adds complexity. How do you reconcile them?\n\n4. **Re-evaluate the Notes System in Light of the Concern:** Can the notes system be adapted to handle this?\n * **\"Non-intuitive notes\":** This is the key insight from the user. The notes *don't* just have to be about the *physical* state. They can represent *narrative* state or *memory*.\n * **Examples of Narrative Notes:**\n * `add_note(room_id, 'event_memory', 'Player convinced Guard Bob by mentioning his lost dog.')`\n * `add_note(player_id, 'quest_log', 'Accepted quest \"Find the Amulet\" from Elder Mary.')`\n * `add_note(npc_id='GuardBob', 'relationship_player', 'grateful_for_dog_mention')`\n * `add_note(world_state, 'major_event_unlocked', 'Orc_Threat_Revealed')`\n * **Function Calls for Narrative:** Actions that change narrative understanding would trigger function calls to add these kinds of notes. \"Persuade Guard Bob\" -> `add_note(...)`.\n\n5. **Compare the Two Approaches (History vs. Enhanced Notes):**\n * **History:** Simpler *conceptually* for the AI (just feed it everything), but has practical limits (tokens, cost) and consistency risks when combined with explicit state.\n * **Enhanced Notes:** More complex *design* (need to decide *what* narrative details are important enough to note), but potentially more scalable, controllable, and enforces consistency better *if implemented well*. It forces you to explicitly define what context is crucial.\n\n6. **Synthesize a Recommendation:**\n * Acknowledge the user's point about history's importance for context.\n * Explain the practical limitations and consistency risks of relying *solely* on full history, especially alongside function-based state updates.\n * Propose the \"enhanced notes\" approach as a middle ground. Use notes not just for physical state but also for key narrative events, relationships, or player knowledge.\n * Suggest that *some* recent history might still be useful (e.g., the last few turns) to provide immediate conversational context, but the *core, long-term state and narrative memory* should reside in the structured notes updated by function calls. This avoids unbounded history growth.\n * Emphasize that this requires careful design of *what* constitutes a significant event worth noting via a function call.\n\n7. **Refine the Language:** Ensure the explanation is clear, addresses the user's specific terminology (\"flow,\" \"non-intuitive notes\"), and presents the trade-offs fairly. Use examples. Conclude by asking if this hybrid approach makes sense." }, { "text": "You've hit on a crucial point and a potential weakness of relying *purely* on discrete state notes! You're right, players interact with the assumption that the GM (the AI) remembers the journey, not just the snapshot of the current room.\n\nLet's break down the \"history vs. notes\" trade-off:\n\n**Keeping the Whole Flow in History (Standard LLM Approach):**\n\n* **Pros:**\n * **Natural Context:** The AI inherently understands the flow of events, dialogue nuances, and *why* things are the way they are because it sees the preceding conversation. This is how LLMs are designed to work.\n * **Simpler Prompting (Potentially):** You might not need prompts that are *quite* as aggressively strict about ignoring un-noted details, as the history provides context.\n * **Handles Implicit Knowledge:** The AI can infer things based on the entire interaction history.\n* **Cons:**\n * **Consistency Risk (The Original Problem!):** This reintroduces the core issue. If the AI relies on history, it's *much* more likely to hallucinate details or contradict the explicit game state you're trying to manage via function calls. Which source of truth does it prioritize if the history implies X but the notes say Y? The function-calling system for state management becomes less reliable.\n * **Context Window Limits:** Game sessions can get long. Conversation histories will eventually exceed the AI's context window limit. Important early events might be forgotten, leading to different kinds of inconsistencies.\n * **Cost/Performance:** Processing a potentially huge history on every single turn is computationally more expensive and slower.\n * **State Management Complexity:** How do you reconcile the implicit state derived from history with the explicit state managed by functions? It becomes messy.\n\n**Relying Primarily on Structured Notes (Your Initial Idea):**\n\n* **Pros:**\n * **Strong Consistency:** If enforced correctly, the AI's output *must* align with the explicit, managed game state. This is the biggest win for reliability.\n * **Scalability:** The state representation (notes) doesn't necessarily grow linearly with conversation length in the same way history does. Context passed to the AI can be curated (e.g., current room notes, player inventory, relevant NPC notes).\n * **Control:** You have precise control over the \"ground truth\" of the game world.\n* **Cons:**\n * **Loss of Narrative Flow Context (Your Point!):** The AI might not understand the *significance* of a state if it only sees the current state. \"The door is unlocked\" - but *why*? Was it picked, was a key used, did an NPC open it? This context might be missing if not explicitly added to the notes.\n * **Requires \"Non-Intuitive\" Notes:** You need to be diligent about using function calls to add notes not just about physical objects, but about *events, knowledge, relationships, and narrative progress*. E.g., `add_note(player, 'knowledge', 'learned_npc_secret')`, `add_note(room, 'event_history', 'player_defeated_guardian')`. This adds complexity to the function/note design.\n * **Rigid Prompting Needed:** Requires very strict prompts to prevent the AI from defaulting to its history-based behavior.\n\n**Finding a Middle Ground (Hybrid Approach):**\n\nPerhaps the most robust solution involves elements of both:\n\n1. **Structured Notes as Primary Ground Truth:** Continue using function calls to update a detailed set of notes representing the canonical game state (physical objects, locations, stats, *and* key narrative flags/events/knowledge).\n2. **Strict Prompting:** Maintain the rule: \"Base your description *only* on the provided notes. Update notes via function calls for *any* state change.\"\n3. **Include *Recent* History:** Provide the AI with the last N turns of conversation history (e.g., the last 3-5 exchanges) *in addition* to the relevant state notes.\n4. **Explicit Narrative Notes:** Make heavy use of function calls to add notes that summarize important narrative beats, discoveries, or relationship changes. `event_happened(event_description)`, `player_learned(fact)`, `npc_relationship_changed(npc_id, change_description)`.\n\n**Why this Hybrid Works:**\n\n* **Consistency:** The strict prompt still forces reliance on the canonical notes for factual details.\n* **Immediate Context:** The recent history gives the AI conversational flow (\"Ah, the player just asked about X, now they're doing Y\").\n* **Narrative Memory:** Explicit narrative notes capture the *significance* of past events without needing the full history. The AI sees `note: player_defeated_guardian` and understands the context even if the fight happened 50 turns ago and is outside the recent history window.\n* **Manageable Context:** You avoid sending the entire, potentially massive history, keeping costs and token limits manageable.\n\n**Conclusion:**\n\nYou are absolutely right to flag the potential loss of narrative context. Relying *purely* on state notes without considering history is brittle. However, relying *purely* on history undermines the consistency goal.\n\nThe solution lies in making the **notes system more comprehensive** to include narrative state, and potentially supplementing it with a **limited window of recent conversation history** for immediate conversational flow, while maintaining the **strict prompting** to prioritize the notes as the ultimate source of truth. This seems like the most reliable path to achieving both consistency and a sense of narrative awareness." }, ] }, { "role": "user", "parts": [ { "text": "INSERT_INPUT_HERE" }, ] }, ], "generationConfig": { "temperature": 0, "responseMimeType": "text/plain", }, "tools": [ { "functionDeclarations": [ ] }, ], } EOF curl \ -X POST \ -H "Content-Type: application/json" \ "https://generativelanguage.googleapis.com/v1beta/models/${MODEL_ID}:${GENERATE_CONTENT_API}?key=${GEMINI_API_KEY}" -d '@request.json'