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LLM Engineering beginner Lesson 1 of 12

LLM Engineering Learning Roadmap

A structured path from your first API call to building production-grade LLM applications — covering prompting, RAG, agents, fine-tuning, and safety.

Why LLM Engineering

LLMs are transforming software. Understanding how to build reliable, cost-efficient, and safe LLM applications — not just prompt a chatbot — is a high-value skill. The gap between a working demo and a production LLM system is large; this roadmap closes that gap.

Stage 1: API Fundamentals (Week 1)

Goal: Make reliable API calls, understand the message format, and control output quality.

Topics:

Milestone: Build a document classifier that correctly categorizes 90%+ of a 100-document test set using a well-crafted system prompt and few-shot examples. Measure accuracy, not just “it looks right.”

Stage 2: Tools and Retrieval (Weeks 2-3)

Goal: Give models access to external tools and knowledge bases.

Topics:

Milestone: Build a RAG chatbot over a 100-page document corpus. Achieve faithfulness >90% (answers grounded in retrieved context, not hallucinated) on a 30-question benchmark.

Stage 3: Context and Scale (Week 3)

Goal: Handle large contexts efficiently and build multi-turn applications.

Topics:

  • Context Engineering — context budget, conversation compression, caching, multi-turn management

Milestone: Build a multi-turn assistant that handles 20+ turn conversations without degrading. Implement context compression that triggers when usage exceeds 70% of the window. Verify the assistant maintains coherence across compression events.

Stage 4: Fine-Tuning and Evaluation (Weeks 4-5)

Goal: Customize models for specific domains and measure performance rigorously.

Topics:

  • Fine-Tuning — when to fine-tune, data preparation, running jobs, evaluating improvements
  • Evaluation — reference metrics, LLM-as-judge, RAG evaluation, regression testing

Milestone: Build an evaluation suite for any LLM application: 50 test cases, LLM-as-judge scoring on 3 dimensions, automated CI runner that fails if score drops >5% from baseline.

Stage 5: Production and Safety (Weeks 5-6)

Goal: Deploy LLM applications reliably, cost-efficiently, and safely.

Topics:

Milestone: Take any LLM application and add: retry with exponential backoff, SQLite-backed response caching, per-model cost tracking, and input/output validation. Measure cache hit rate and cost reduction.

Stage 6: Projects

Beginner: Smart summarizer, code review bot, FAQ answer bot
Intermediate: RAG documentation assistant, automated code generator with tests, semantic search engine
Advanced: Multi-agent research system, fine-tuned domain expert, LLM test automation framework

See LLM Engineering Projects for full descriptions.

What Comes After LLM Engineering

  • AI Agents — autonomous systems that use tools and plan multi-step
  • MLOps for LLMs (LLMOps) — prompt versioning, cost dashboards, evaluation pipelines
  • Fine-tuning at scale — LoRA, PEFT, full fine-tuning with accelerate
  • Vector databases — Weaviate, Pinecone, Qdrant for production RAG

Resources

  • Anthropic documentation: docs.anthropic.com
  • “Building LLM Applications” (various online courses)
  • LangChain and LlamaIndex docs — for RAG patterns
  • OpenAI Cookbook — transferable recipes despite different API

Frequently Asked Questions

Do I need to understand how transformers work to build LLM applications?
Not deeply. You need to understand context windows (tokens limit what the model can see), temperature and sampling (how randomness affects outputs), and that models predict the next token (so output quality depends heavily on framing). You don't need to understand attention mechanisms or model weights to build good applications.
What's the most common mistake when building LLM apps?
Skipping evaluation. It's easy to get a demo that works on 5 hand-picked examples and assume the application is ready. Build a test suite of 50+ representative cases early, score outputs systematically, and track your pass rate as you iterate. Without this, every change is a guess.