1. What is prompt engineering?
How clear instructions, examples, constraints, and output formats help an AI model complete a task.
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Prompt & Context Engineering
Learn how prompts, context, retrieval, memory, and agent state shape model quality, token usage, latency, and inference cost.
18 free published lessons, no account required. Some lessons are cross-listed across tracks. Who this is for: Product teams, developers, analysts, founders, operators, and anyone building AI applications or agents.
Lesson order
Recommended lesson order
Work through these lessons in sequence to build a usable understanding of this AI compute topic.
2. Define the AI task and success criteria
Turn a vague AI request into a measurable task with a clear outcome, constraints, and acceptance criteria.
3. Prompt anatomy: instructions, context, and output
Learn the main parts of a production prompt and how each one affects model behavior, tokens, and validation.
4. Clear instructions and constraints
Write direct AI instructions with priorities, boundaries, edge cases, and constraints that reduce ambiguity without creating a brittle prompt.
5. Few-shot examples and counterexamples
Learn when examples improve AI behavior, how to choose representative cases, and how example tokens can change workload cost.
6. Structured outputs and output contracts
Use schemas, required fields, and deterministic validation to make AI responses usable by software.
7. Prompt decomposition and chaining
Break complex AI work into smaller stages and decide which steps belong in code, prompts, or human review.
8. Prompt versioning and regression testing
Track prompt changes, test representative cases, and detect quality or workload regressions before release.
9. What is context engineering?
How AI systems select and manage the information a model receives before making a decision.
10. Context windows and token budgets
Understand the finite input and output budget shared by instructions, history, retrieved data, and model responses.
11. What belongs in context?
Select the smallest set of relevant, authoritative, recent, and complete information needed for the next AI decision.
12. Retrieval-augmented generation explained
Understand how an AI application retrieves external information and supplies it to a model before generation.
13. Chunking and document preparation
Prepare documents and retrieval chunks so relevant information stays together, searchable, and traceable.
14. Ranking, reranking, and retrieval quality
Learn how retrieval systems order candidate evidence and how to measure whether the right material reaches context.
15. Long context is not the same as good context
Learn why adding more tokens can increase cost and distraction without improving an AI answer.
16. Conversation history, state, and memory
Separate recent messages, durable facts, task state, and retrieved knowledge in a long-running AI workflow.
17. Context compression, caching, and reuse
Reduce repeated context work with summaries, cached prefixes, and reusable state while preserving important evidence.
18. Untrusted context and prompt injection
Keep user text, retrieved documents, tool results, and memory from silently overriding the instructions that govern an AI system.
Market signal
How this track helps you read the AI compute market
Prompt and context design determine how many tokens an application processes, how often it retries, how many tools an agent calls, and whether a smaller model can complete the task. Those choices affect latency, memory use, throughput, and cost per completed task. This track helps readers connect application design decisions to recurring inference demand.
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