Fineuralab

LLM Roadmap

A Fineuralab roadmap for learning practical LLM applications through prompting, context design, RAG basics, evaluation, and failure logs.

LLM roadmap

Route overview

This path turns LLM 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 want to build useful AI tools without pretending every answer is correct.
  • You need a path from prompting to evaluable LLM applications.
  • You care about cost, privacy, output review, and failure modes.

Not for

  • You only want prompt tricks with no evaluation.
  • You want large-scale model training infrastructure first.

Prerequisites

  • Basic API usage and JSON
  • Ability to define task inputs and expected outputs
  • A willingness to keep failure examples

Study plan

4-week minimum

  1. Week 1: task framing, prompt boundaries, and output contracts.
  2. Week 2: context packing, source notes, and token budget.
  3. Week 3: small RAG prototype with 10 test questions.
  4. Week 4: evaluation table, failure categories, and revision plan.

8-week extension

  • Add routing, refusal rules, citation checks, human review steps, and a small regression test set.
  • Finish by comparing two prompts or models against the same evaluation set.

Final output

Build a document QA or writing-assistant workflow with test cases, known failures, cost notes, and privacy boundaries.

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 Learning Path BuilderGenerate a local 4, 8, or 12 week learning path for deep learning, LLM applications, RAG and agents, paper reproduction, AI engineering, or AI product building based on your level, goal, time budget, and preferred learning style. AI Context WorkbenchTurn messy notes, logs, errors, and requirements into a structured AI-ready context brief. AI Eval Dataset BuilderTurn an AI task, pass criteria, and real failures into a maintainable JSONL eval dataset with judge focus notes. LearningA project-first AI, deep learning, LLM, RAG, and paper reproduction learning hub.

Reviewed and updated: June 26, 2026