22. Working With Documents and Large Text

What this section is for

Most real work involves long text: specs, docs, tickets, policies, legal language, logs, RFCs, and knowledge bases.

This section teaches you how to work with long documents in AI Studio without the two classic failures:

• Context flooding: pasting too much and getting shallow or wrong answers.
• Ungrounded synthesis: answers that sound right but can’t be traced back to the source.

Why long-text work fails

Long-text tasks fail for predictable reasons:

• Context limits: even large context windows have a budget; pasting everything competes with your instructions.
• Attention dilution: the model may ignore the key paragraph you care about.
• Contradictions: docs often contain multiple versions of the “truth.”
• Missing provenance: if you can’t trace claims to sources, you can’t verify.

A strategy ladder: paste → summarize → chunk → retrieve

Use this ladder to pick the simplest approach that works:

1. Paste (small doc): if it’s short enough, include it directly, but still demand citations.
2. Summarize: if you need the gist and can tolerate some loss, summarize with structure and keep a link to the source.
3. Chunk: split into meaningful chunks with metadata so you can reference sections.
4. Retrieve: for repeated Q&A, use retrieval so you only include relevant chunks.

Most “doc assistant” prototypes start at chunking and move to retrieval once the loop is valuable.

Artifacts that keep you grounded

Long-text work becomes reliable when you create a few simple artifacts:

• Chunk index: list of chunk ids with titles/section paths.
• Citation format: a stable way to reference chunks (e.g., [doc:3.2]).
• Contradiction notes: a small ledger of conflicting statements and which one you treat as current.
• Evaluation set: 25–50 questions that matter, used to regression-test changes.

Section 22 map (22.1–22.5)

• 22.1 Chunking strategies that preserve meaning
• 22.2 Building a “document Q&A” assistant prototype
• 22.3 Citation-like behavior (trace outputs to sources)
• 22.4 Handling contradictions and multiple versions
• 22.5 Long-context performance and cost tradeoffs

Where to go next

• Start with chunking (22.1)
• If you’re ready for RAG, jump to Part VIII