LLM Engineering Learning Roadmap

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, from how the model itself works up through serving it at scale.

Stage 1: Foundations (Week 1)

Goal: Understand what’s actually happening inside a model call, before treating it as a black box.

Topics:

• How an LLM Actually Works — tokens, embeddings, transformer blocks, attention, next-token probabilities
• Tokenization — byte-pair encoding, why languages cost different token counts, special tokens
• Inference and Decoding — temperature, top-k, top-p, repetition penalty, why temperature 0 isn’t “more certain”
• Pretraining, Instruction Tuning, and Alignment — why a base model and a chat model behave completely differently on the exact same weights

Milestone: Given a model’s logits for a prompt, correctly predict how greedy decoding, temperature=1.5, and top-p=0.9 would each change the output — then verify against real generated text.

Stage 2: API Fundamentals (Week 1-2)

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

Topics:

• Introduction to LLM Engineering — API basics, message roles, tokens, temperature, streaming
• Prompt Engineering — system prompts, few-shot examples, chain-of-thought, XML structuring
• Structured Outputs in Production — schema validation and a retry loop for when “return JSON only” doesn’t happen
• LLM APIs and Provider Abstraction — a common interface over multiple backends, with real timeout-and-fallback routing

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 3: Tools and Retrieval (Weeks 2-3)

Goal: Give models access to external tools and knowledge bases, and know how to fix retrieval when it’s wrong.

Topics:

• Tool Use — tool definitions, parallel calls, error handling, multi-turn tool use
• RAG — Retrieval-Augmented Generation — chunking, embeddings, vector search, source attribution
• Embeddings — similarity search, clustering, semantic deduplication
• RAG: Advanced Retrieval and Reranking — hybrid search, cross-encoder reranking, multi-query retrieval
• RAG Failure Diagnosis — bad chunking, stale documents, context overload — diagnosed, not just described
• Multimodal LLMs — sending images and documents to a model, with a real cross-modal matching example

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, then deliberately break it one way from RAG Failure Diagnosis and fix it.

Stage 4: 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
• Long-Context Economics and Prompt Caching — measured latency/cost scaling, and when caching a prefix actually pays off

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

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

Topics:

• RAG vs. Fine-Tuning vs. Prompting — a decision framework, and one problem solved two ways to show the failure only grounding fixes
• Fine-Tuning — when to fine-tune, data preparation, running jobs, evaluating improvements
• LoRA, QLoRA, and PEFT — the DIY/open-model alternative to a hosted fine-tuning API, training <1% of parameters
• 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 6: Inference and Serving (Week 5-6)

Goal: Understand what a hosted API and a self-hosted model server are actually doing underneath the HTTP layer.

Topics:

• Quantization — FP32/FP16/INT8/INT4 trade-offs, GPTQ/AWQ/GGUF, and how QLoRA combines with fine-tuning
• LLM Inference and Serving — KV cache, prefill vs. decode, batching — what vLLM/TGI/Ollama automate

Milestone: Measure your own model’s tokens/sec with and without KV caching, and with batch size 1 vs. 4+, to see the two biggest levers real serving stacks pull.

Stage 7: Production and Safety (Weeks 6-7)

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

Topics:

• Production Patterns — retry/backoff, caching, cost tracking, model routing
• Safety and Guardrails — input validation, output validation, rate limiting, injection detection
• LLM Security Beyond Guardrails — indirect prompt injection, excessive agency, insecure output handling

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 8: 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
• Vector databases at scale — Weaviate, Pinecone, Qdrant for production RAG beyond a single-machine vector store

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
• Hugging Face documentation — transformers, peft, and quantization libraries used in the Foundations and Inference stages