Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== Assistant: Exactly — that’s a practical and effective approach when working with a small model. ===
Exactly — that’s a practical and effective approach when working with a small model. Let’s break it down clearly and practically:

==== - Small models have limited neurons / attention heads, so they cannot process thousands of pages at once. ====
* Long context windows help, but the model can only meaningfully reason over a fraction of that context at a time.

==== 1. Pre-process your project - Break content into small, manageable units (sentences, paragraphs, sections). - Assign tags / metadata to each unit: - Topic, chapter, subcategory - Priority / importance - Any dependencies ====
# Feed the model incrementally - Send one sentence or small chunk at a time, along with its tags. - Example input to AI:

<syntaxhighlight>[Tag: Chapter 3 | Subtopic: Materials | Priority: High]
Sentence: "The new alloy exhibits a tensile strength of 1,200 MPa at 500°C."
Task: Summarize key points, link to previous notes tagged with Materials.

</syntaxhighlight>
# Use external memory / orchestration - Store AI outputs, summaries, or insights in a structured database. - Include the tags so the model can retrieve relevant context later.
# Iteratively build understanding - For each new chunk, feed in: - The chunk itself - Relevant previous notes retrieved via tags - Task instructions for what to do with it

==== - Small AI only processes a few ideas at a time, staying within its “ideas bucket.” ====
* Tags allow the orchestrator to link chunks together, so the AI can maintain coherence across thousands of pages without exceeding its capacity.
* Incremental feeding + external memory simulates a much larger context window than the model can handle internally.

==== - Summarization hierarchy: periodically compress older notes into concise summaries. ====
* Cross-referencing: when new chunks are processed, include relevant summaries from related tags.
* Chunk prioritization: feed critical content first, low-priority content later.
* Prompt engineering: explicitly instruct the AI on how to integrate new info with past notes.

===== - Think of your small AI as a student with a tiny desk: - You can’t fit all the books on the desk at once. - You give it one page at a time, with labeled notes and summaries. - It writes its own summaries into a notebook (external memory). - Next page, it consults the notebook to understand the context. =====

If you want, I can make a diagram showing a “small AI + large project” workflow, with incremental feeding, tagged chunks, and external memory retrieval — showing exactly how you can scale thousands of pages for a small model.

Do you want me to do that?