Fineuralab

RAG and Agent Roadmap

A Fineuralab roadmap for learning RAG and agent workflows through retrieval design, tool contracts, memory boundaries, permissions, and evaluation.

RAG roadmap

Start with one verifiable loop

This path turns RAG learning into a minimum route, a project route, and one final output. Prove progress first, then decide whether to go deeper.

4 week minimum 8 week project route 1 final output

Who this is for

Good fit

  • You already know what an LLM app is and want stronger retrieval or tool-use workflows.
  • You need agent workflows that are inspectable instead of magical.
  • You care about permission gates and recovery when the agent gets stuck.

Not for

  • You have not built a simple LLM app yet.
  • You want fully autonomous agents without human confirmation.

Prerequisites

  • Basic LLM API use
  • Chunking, embeddings, and retrieval vocabulary
  • Ability to describe tool inputs, outputs, and failure states

Study plan

4-week minimum

  1. Week 1: chunking and retrieval baseline.
  2. Week 2: answer grounding and source conflict handling.
  3. Week 3: tool-call contract with explicit permissions.
  4. Week 4: recovery plan, logs, and evaluation set.

8-week extension

  • Add reranking, memory limits, user confirmation checkpoints, incident notes, and multi-step task evaluation.
  • Finish with a public demo that shows where automation stops.

Final output

Build a small agent workflow with retrieval, one tool call, human confirmation, rollback notes, and a failure replay.

Proof of learning

  • Keep inputs, prompts, code, metrics, and failure samples.
  • Write a short weekly review: what you learned, what you misjudged, and how next week changes.
  • The final report should name what was not done, so a demo is not overstated as full capability.

Tools for this path

AI Knowledge Base Chunking PlannerPlan chunk boundaries, metadata fields, exclusion rules, and redaction checks before turning docs, FAQs, transcripts, policies, or code into a RAG knowledge base. AI Tool-Call Contract BuilderCreate a tool-calling contract for AI agents: tool list, parameters, returns, errors, permission tiers, dry-run rules, human approval, audit logs, and rollback checks. AI Agent Permission GateCheck whether an AI agent should be allowed to browse, run commands, write files, deploy, access accounts, or touch production systems. AI Agent Run Recovery PlannerPaste a stuck AI agent transcript, command output, or failure log. The tool identifies loops, permission blocks, test failures, stale context, and creates a recovery prompt. LearningA project-first AI, deep learning, LLM, RAG, and paper reproduction learning hub.

Reviewed and updated: July 6, 2026