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Querying Documents with LLMs Instead of Reading Them

Reading a technical doc cover-to-cover is like running a full table scan. A 64-page guide might only have five pages of signal, but you grind through all 64 anyway.

I tried something different on Google’s Startup technical guide: AI agents. Instead of reading, I queried it. Treat the doc like a database.

This isn’t deep reading. It’s triage.

LLM → concise insights

# The Workflow

The point: figure out in minutes if a doc is worth hours. Two steps: broad “indexing” → targeted follow-ups.

# 1. The Indexing Query

Ask the LLM to map the doc. Thesis, sub-arguments, audience, frameworks. Smarter than a TOC:

Analyze this document. Identify its primary thesis, its secondary arguments, the intended audience, and any specific technical frameworks or code it provides. Present this as a structured summary.

A raw output might look like:

with some rambling

Based on an analysis of the document, here is a structured summary.

# Primary Thesis

The central argument of this guide is that for startups to succeed with AI, they must move beyond simple prototyping to a disciplined, engineering-driven methodology for building, deploying, and managing production-grade AI agents. This systematic approach, centered on the Google Cloud ecosystem, enables the creation of reliable, scalable, and responsible agentic systems, which provides a significant competitive advantage.

# Secondary Arguments

…

# Intended Audience

…

# Technical Frameworks and Code

  • Primary Development Framework: The Agent Development Kit (ADK)
  • Operational Implementation: The Agent Starter Pack
  • Key Protocols:
    • Agent2Agent (A2A)
    • Model Context Protocol (MCP)
  • Recommended Runtime: Vertex AI Agent Engine
  • No-Code Alternative: Google Agentspace
  • Code Provision: The guide is conceptual and does not provide direct code snippets

ADK Samples repository for working code examples.

Already clear: this is high-level, conceptual. No code here. That alone saves time.

# 2. The Targeted Query

Now pull a thread:

You mentioned “must move beyond simple prototyping to a disciplined, engineering-driven methodology for building, deploying, and managing production-grade AI agents”. Provide a comprehensive list of criteria for what “disciplined” and “engineering-driven” mean in this document.

This pins the model to the source and shows if the doc has substance. After a few rounds, you know if it’s worth a real read.

# Limitations

This works as a filter. It breaks if you trust it blindly.

  • Hallucinations happen. I once asked about a new database’s WAL protocol. The model said ARIES. Wrong – the paper invented a new one. It only compared to ARIES. Always cross-check.
  • Nuance dies. Author’s pacing, careful build, caveats – gone. Don’t use this for philosophy, law, or anything where structure is meaning.
  • Complexity flattens. A summary makes a hard thing look simple. It’s navigation aid, not expertise.

# Conclusion

This isn’t reading. It’s interrogation. Use it to slash through the pile of PDFs and whitepapers. Then only read the ones that deserve your hours.