AI Engineering Map
Core ideas
- Tokens and context windows
- Embeddings and semantic search
- Sampling, temperature, and structured output
- Tool calling and agent loops
- Retrieval-augmented generation
- Evaluation datasets and graders
- Latency, cost, caching, and fallbacks
- Safety, privacy, prompt injection, and data boundaries
Build loop
flowchart LR
A[Define task and success] --> B[Create representative evals]
B --> C[Build simplest prompt or workflow]
C --> D[Measure failures]
D --> E[Improve context, tools, or model]
E --> C
Durable principles
- Start with a measurable task, not a fashionable architecture.
- Treat prompts, tools, model choice, and retrieved context as versioned code.
- Separate trusted instructions from untrusted content.
- Require evidence for claims that affect users or irreversible actions.
- Evaluate on realistic edge cases before optimizing average performance.
Related
- DeepLearningAI Agentic AI Course
- Agentic AI Real-World Applications
- Agentic AI Development Loop
- Spec Map Build Prove Learn
- Agentic Fullstack Development Webinar
- Prompt Library
- Agent Rules
- Security Review