AI Engineering and Research
Applied guidance for RAG, evaluation, fine-tuning, deployment, observability, and the production engineering decisions behind reliable AI systems.
Production AI is a system design problem. Model capability matters, but reliability also depends on data quality, retrieval, evaluation, latency budgets, observability, security, and the way failures are handled.
This hub connects research concepts with implementation decisions. It focuses on architectures that can be tested, operated, and improved instead of patterns that work only in isolated demonstrations.
Choose architecture from evidence
Teams should begin with a measurable baseline and add complexity only when the evidence justifies it. Prompting, structured context, retrieval, fine-tuning, and agents solve different problems and carry different operational costs.
- Define the target behavior and simplest credible baseline.
- Use retrieval when current or private knowledge must be grounded.
- Use fine-tuning only when repeated behavior cannot be solved more simply.
Treat evaluation as a product contract
Evaluation connects model behavior to user expectations. A useful program combines automated checks, expert review, production feedback, and regression suites that reflect the real distribution of requests.
- Build versioned test sets from representative and difficult cases.
- Measure groundedness, correctness, safety, latency, and cost together.
- Run regression checks whenever prompts, models, or data pipelines change.
Operate the complete system
Reliable AI services require observability beyond uptime. Teams need visibility into model versions, retrieval quality, tool calls, token usage, fallbacks, and user-visible failure modes.
- Trace requests across retrieval, model, and tool boundaries.
- Set budgets and alerts for latency, errors, and usage anomalies.
- Design fallbacks, human review, and rollback paths before launch.