The Agentic RAG Workflow Rewriting Code Development Rules

Dear Reader…

NotebookLM Is Not What You Think It Is!

Chances are that you’ve filed NotebookLM away in the "learning tools" drawer. A place to summarise PDFs, accelerate your learning on a subject, or maybe onboard yourself onto a new framework over lunch. A workflow is emerging in the Claude Code community that repositions NotebookLM as something far more consequential - a source-grounded intelligence engine sitting at the heart of an agentic code development pipeline. When paired with Claude 4.6 and a handful of bridging tools, NotebookLM stops being a study aid and starts functioning as the senior engineer who has actually read every page of the documentation you have been meaning to get to for the last months and years.

This week, we are going deep on how NotebookLM works as institutional memory for code development and data management, why it matters for your day-to-day, and how you can set it up.

The Problem This Solves

If you have spent any serious time with Claude Code, you will have hit the context ceiling. Claude 4.6 Opus supports a 200k token window, which sounds enormous until you start feeding it a real-world data platform codebase alongside API documentation, internal READMEs, and the half-dozen Confluence pages explaining why somebody made that bizarre architectural decision three years ago.

What happens next is predictable - context decay. The model starts "forgetting" your architectural constraints as recent terminal output floods the window. It hallucinates API parameters and invents function signatures. You spend more time correcting the agent than you would have spent writing the code yourself.

The root cause is simple. We have been asking one tool to do two fundamentally different jobs: 1. Store vast documentation, and 2. Execute precise code changes. This NotebookLM workflow splits these responsibilities into a federated intelligence architecture. Claude handles execution. NotebookLM handles knowledge. The result is dramatically better than either tool alone.

How the Pipeline Works

The workflow has three stages.

1️⃣ Stage one is repository distillation. A utility called Repomix consumes your entire codebase and compresses it into a single, tokenised markdown file, stripping out lock files, binaries, and build artefacts to keep only the logic and structure.

2️⃣ Stage two is knowledge base construction. You upload the tokenised repository to NotebookLM alongside supporting documentation using "triangulated documentation": authoritative sources (official API docs, whitepapers), practical sources (video transcripts, code walkthroughs), and contextual sources (internal READMEs explaining the reasoning behind implementation choices). NotebookLM synthesises these into a unified, queryable interface powered by Gemini's long-context capabilities, processing up to 1,000,000 tokens simultaneously.

3️⃣ Stage three is the bridge. A Claude Code "skill" called notebooklm-skill uses browser automation to let Claude chat directly with your NotebookLM notebooks. When Claude encounters a question during execution, it autonomously queries NotebookLM, receives a citation-backed answer grounded in your documentation, and continues working. No copy-pasting, no tab switching, no context window pollution. The skill uses a stateless design: open browser, ask question, retrieve cited answer, close session. This prevents session bloat and keeps Claude's context window clean for reasoning.

The Numbers That Matter

A typical query through the NotebookLM skill costs approximately 3,000 tokens. Reading raw documentation directly into Claude's context can consume 50,000 to 100,000 tokens per turn. For data engineers running multi-step workflows against large codebases, this is the difference between pennies per interaction and burning through your API budget before lunchtime.

But the real win is reliability. Standard RAG implementations rely on vector search chunking, which creates retrieval gaps. NotebookLM's Gemini engine reads all sources holistically and often refuses to answer if the information is not present, meaning Claude cannot hallucinate non-existent API parameters. For anyone who has spent an afternoon debugging a function call the AI confidently invented from nothing, this behaviour is worth its weight in gold.

NotebookLM as Your Codebase's Thinking Partner

Here is where the framing shift matters. NotebookLM is not just synthesising documentation. It is acting as an intelligent layer that understands how your codebase works.

One of the most compelling use cases is legacy codebase recovery. Every data engineer knows the pain of returning to a pipeline written years ago. The original developer has moved on. The documentation is sparse. The NotebookLM approach treats your entire codebase as if it were onboarding a new team member. Upload the tokenised code alongside the README, then instruct the AI to explain the architecture, trace dependencies, and identify where you can safely modify logic. Ask "Where are the pricing calculations coupled to the ETL layer?" and receive RAG-based grounded answers citing specific files. Beyond this the audio overview and briefing slide capabilities mean that new personnel can come up to speed on the architecture and intent behind the code base in a totally different way to before.

Practitioners report using this to generate "Engineering Briefs" that map system architecture and flag risks before code changes begin. These briefs feed directly into CLAUDE.md, the project-root file Claude Code reads at the start of every session, creating a seamless handoff from research to execution.

The Hand-off Pattern for Teams

For data engineering teams, this handoff pattern is transformative. First, use NotebookLM to produce a one-to-two page Engineering Brief from the triangulated documentation. Second, paste the brief into the project's CLAUDE.md, giving the agent grounded instructions. Third, initiate Claude Code with a directive: "Follow the Engineering Brief in CLAUDE.md. If unsure about API parameters, query the NotebookLM skill."

With Claude 4.6's multi-agent capabilities, you can assign a lightweight "Researcher Agent" on Haiku 4.5 to handle NotebookLM queries while the primary "Builder Agent" on Opus 4.5 focuses on implementation. Verified, cited information flows to the builder in parallel, keeping your high-reasoning engine focused on code rather than documentation lookup.

The Honest Constraints

This workflow is not perfect. NotebookLM is a static ingestion engine. When Claude makes code changes, those changes are not automatically reflected in the notebook. You need to re-run Repomix and re-upload. This makes the approach best suited for stable libraries, documentation-heavy frameworks, and legacy systems rather than codebases shifting hourly.

Browser automation through Playwright also introduces latency of several seconds to a minute per query. And uploading proprietary code to Google's infrastructure raises privacy concerns your security team will rightly want to scrutinise.

Why This Matters Now

The convergence of Claude 4.6's agentic execution, NotebookLM's source-grounded synthesis, and bridging tools like MCP represents something genuinely new: the end of context switching for the data engineer. Instead of jumping between ten applications to find an API parameter, you remain in a single terminal flow, commanding specialised tools that collaborate behind the scenes.

This is not about replacing your expertise. It is about eliminating the friction that prevents you from applying it. The documentation lookup, the re-learning curve, the copy-paste dance between browser and terminal. All of it collapses into a unified workflow where your agent has the knowledge base a senior engineer carries in their head, except it never forgets and always cites its sources.

The strategic move is clear, we recommend starting to build (or at least experiment with) your NotebookLM knowledge bases now. Populate them with framework documentation, internal architectural decisions, and tokenised codebases. The chances are that teams that master this federated approach will build faster, debug more reliably, and spend their cognitive energy where it matters. This is the architecture and the thinking, not the lookup.

That’s a wrap for this week

Happy Engineering Data Pro’s