AI reliability
Bulkheads, structured outputs, retries, guardrails, and the habits that keep demos from hurting real users.
Series
Effective AI Engineering
A production-minded reading path for building AI systems that are reliable, observable, evaluable, and safe enough to put in front of real users. This series starts from the Mirascope Effective AI tips corpus, but only promotes pieces once they have a useful home in the library.
The shape of the series
- 1Make AI calls observable before trying to optimize them.
- 2Turn traces, annotations, and replay into an improvement loop.
- 3Harden RAG, agents, and tool use around the places production systems actually fail.
Published path
No Effective AI Engineering pieces are published yet. The route, structure, and curated queue are ready so the first batch can land without turning the library into a content junk drawer.
First TIL batch candidates
-
Build Bulkheads Around Your AI Calls
AI reliability -
Instrument Your AI Calls
Evals observability -
Don’t Just Log, Annotate!
Evals feedback loops -
Structure Your Outputs for Reliable Systems
AI reliability -
Isolate & Evaluate Your RAG Retriever
RAG evals -
Quality Control Your RAG Chunks
RAG -
Break Complex Tasks into Evaluable Components
Agents evals -
Output Guardrails
Security AI reliability -
Citation Validation
RAG AI reliability -
Record and Replay
Evals testing -
Human Approval for Risky Tools
Agents security -
Sandboxed Code Agents
Agents AI coding security -
Graph-Based Agent Workflows
Agents
Topic clusters
Evals + observability
Instrumentation, annotation, record/replay, and decomposing fuzzy work into reviewable components.
RAG + retrieval
Retriever evals, chunk quality, citation validation, reranking, and query rewriting.
Agents + workflows
Approval gates, sandboxes, state machines, and safer tool-using workflows.
Best next move
Start with instrumentation, annotation, and record/replay. Those pieces make the feedback loop visible, which gives the rest of the series somewhere concrete to point.
Where this fits
Use the Library as the front door and the AI Evals hub as the first topic anchor.