Topic guide
AI Engineering
AI engineering is the discipline of turning model capabilities into systems that remain useful under real data, users, and failure modes. That requires more than prompts: context assembly, evaluation, product constraints, observability, and conventional software engineering still determine reliability.
The practical questions
- Building reliable context around language models
- Evaluating outputs when conventional tests are insufficient
- Using AI for bounded transformations rather than vague magic
- Delivering AI features inside maintainable software systems
Articles on AI Engineering
6 articles, ordered by topical relevance.
Evals Are Not Tests
Unit tests tell you when code breaks. Evals tell you if your LLM is still answering the right question. These are different problems, and treating them the same way is how AI products degrade silently.
ai · llms · software engineering · braintrustAI Tools Are Not Magic Bullets
LLMs are not magic bullets. They are powerful translation engines for messy data, especially when you use them to summarize, structure, and move information between systems.
ai · llms · data · software engineering · automationSprints Are the Wrong Unit for AI Delivery
MCP and LLM tooling make delivery, reporting, and testing more continuous. That changes what teams should optimize for.
ai · llms · software deliveryWhy RAG Fails in Production
Most RAG systems fail at retrieval, not generation. The fix is usually better evidence, ranking, and product logic.
ai · llms · architecture · software engineeringContext Engineering Is Not Prompt Tuning
Prompt engineering focuses on the question. Context engineering focuses on the world you hand the model before the question. That distinction changes how you design reliable LLM systems.
ai · llms · software engineering · platform engineeringAI Scalability: What It Actually Means for Software Architecture
AI can generate working software quickly, but AI scalability depends on architecture, constraints, and disciplined engineering after the proof of concept.
architecture · scalability · ai · software engineering