150 lines
7.2 KiB
Markdown
150 lines
7.2 KiB
Markdown
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# Explanation: ARAI’s Prompt Chaining Approach
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**ARAI AI Agents** employs a narrative-based prompt chaining methodology to create cohesive, non-repetitive, and engaging content—ranging from **tweets** and **social media posts** to entire **story arcs**. This approach draws inspiration from **Hollywood screenwriters**, using seasons and episodes to structure continuous storylines and maintain context.
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---
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## Table of Contents
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1. [Core Concept](#1-core-concept)
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2. [Why “Prompt Chaining”?](#2-why-prompt-chaining)
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3. [High-Level Workflow](#3-high-level-workflow)
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4. [The Importance of a Large Context Window](#4-the-importance-of-a-large-context-window)
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5. [Example Flow](#5-example-flow)
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6. [Benefits of This Approach](#6-benefits-of-this-approach)
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7. [Conclusion](#7-conclusion)
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---
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## Core Concept
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1. **Non-Repetitive Storytelling**
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- Each generated post builds on previous context, avoiding repetitive language or ideas.
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- The AI references a “universe” or “world Bible” similar to what screenwriters use in film or television.
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2. **TV Show & Cinematic Structure**
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- Stories are segmented into **seasons** and **episodes**.
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- Each **episode** can be further divided into “scenes,” which are effectively _individual posts_ or _tweets_.
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3. **Prompt Chaining**
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- **Prompts** are carefully crafted to pass relevant context (e.g., last episode events, the overall season arc).
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- **Chained prompts** ensure that each subsequent piece of content knows what has happened before and maintains consistency.
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---
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## Why “Prompt Chaining”?
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Typical AI-generated content can become repetitive when prompts are not carefully managed. **Prompt chaining** solves this by:
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- **Carrying Over Context**
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Each step (post, scene, or episode) includes details of what came before, preventing “resetting” or “forgetting” and ensuring a natural flow.
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- **Layering Background & Universe Data**
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The AI “knows” the characters’ personalities, the setting, and prior events. This leads to more believable and varied outputs.
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- **Batch & Story-Based Generation**
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Instead of randomly generating single posts, ARAI processes entire sequences of posts together, referencing each other for narrative coherence.
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---
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## High-Level Workflow
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1. **Character Background Sheets**
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- The system prompts the AI to create detailed character profiles:
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- **Universe & Backstory** (setting, tone, etc.)
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- **Traits** (personality, style, goals)
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- **Emojis & Hashtags** they might use
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2. **Season & Episode Creation**
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- **Season** = a broad story arc (e.g., Season 1: “The Origin”).
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- **Episodes** = subdivisions in each season (e.g., Episode 1: “Awakening”, Episode 2: “New Allies”).
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- This structure emulates a TV series format, providing an expansive canvas for narrative progression.
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3. **Scene-to-Post Conversion**
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- Each **episode** is broken down into “scenes,” which **map directly to individual social media posts**.
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- _Context injection_ includes:
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- Last episode’s key events
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- Character developments
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- The overall “season” summary
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4. **Batch Generation**
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- ARAI then prompts the AI to generate multiple posts at once (or in succession) so they share context and maintain narrative continuity.
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- This ensures each post references the correct timeline and plot details.
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5. **Season Rollovers**
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- After a season ends, **context** from that entire season is folded into the AI’s prompt for the next season.
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- This preserves continuity across seasons, allowing characters to evolve over time.
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---
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## The Importance of a Large Context Window
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To make prompt chaining truly effective, the underlying AI model needs a large **context window**. The **context window** refers to the amount of text (measured in tokens) that the model can “remember” and consider when generating a response.
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- **Why We Use `2.0 Experimental Advanced model in Gemini Advanced.`**
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By default, ARAI uses the `Gemini-Exp-1206` model because it offers a large context window. This is ideal for our narrative-driven approach because:
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- **Long-Term Memory:**
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The model can retain information from earlier parts of the conversation (e.g., details from previous episodes or seasons), which is crucial for maintaining consistency in long-running storylines.
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- **Complex Narrative Structures:**
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A larger context window allows the model to handle intricate plots, multiple characters, and evolving relationships within the narrative.
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- **Reduced Repetition:**
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With more context available, the model is less likely to fall back on repetitive phrases or generic responses.
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- **Model Selection and Context Window:**
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When choosing a model for ARAI, the size of the context window is a primary consideration. While a larger context window generally improves performance on complex tasks, it can also increase computational cost and latency. The `gemini-pro` model provides a good balance between context size and efficiency for our use case.
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---
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## Example Flow
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1. **Initialize a “Concept of AI Agent”**
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```plaintext
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Prompt:
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"You are a comedic AI sidekick from a futuristic Mars colony. Generate a backstory
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detailing your origin, personality traits, and comedic style. Also include an
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emoji palette and hashtags you frequently use."
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```
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2. **Generate a Character Sheet**
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- AI responds with backstory, style notes, emojis, hashtags (`#MarsLife`, `#CosmicComedy`, etc.).
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3. **Create a Season Plan**
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- _Season 1: “Launch Day”_ with 5 episodes.
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4. **Episode 1**
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- Provide the AI with _scene outlines_ or _beats_ to cover in episode 1.
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- AI generates Scenes 1, 2, 3, each as separate social media posts but referencing each other’s details.
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5. **Proceed to Episode 2**
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- Summarize Episode 1 outcomes: “In Episode 1, your comedic AI discovered a stowaway on the Mars rocket…”
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- The AI crafts next scenes with knowledge of Episode 1’s revelations.
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6. **Season Finale**
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- Summaries of all episodes in Season 1 inform the _Season 2_ kickoff prompt.
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---
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## Benefits of This Approach
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- **Narrative Consistency**: By continuously chaining prompts, the AI won’t “forget” critical details and the story remains coherent.
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- **Creative Expansion**: You can easily scale from short comedic sketches to grand multi-season arcs.
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- **Flexibility**: Adapt the same system for _comics_, _film scripts_, or _novel chapters_, just by tweaking prompts.
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- **Engagement**: Social media followers can follow an unfolding storyline rather than seeing disjointed or repetitive posts.
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---
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## Conclusion
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**ARAI AI Agents** leverages a _story-first, chain-of-thought approach_ to generating content. By structuring the process akin to **Hollywood screenwriting** and dividing it into **seasons, episodes, and scenes**, ARAI creates vibrant, interconnected narratives. Each step references previous context, preventing repetitive output and fostering deeper engagement for readers (or social media audiences).
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If you’d like to learn more about setting up your environment or configuring connectors:
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- Check our [How-To Guides](./how-to-guides.md) for environment & API key setup
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- Look at the [Tutorials](./tutorials.md) for step-by-step instructions on building your first season-based storyline
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**Happy storytelling with ARAI!**
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