8 min readApplied
AI Engineer Interview Guide 2026: Build These 5 Apps
An AI engineer interview guide built around five portfolio apps, the questions each one answers, and why the RAG app is the one candidates get wrong.
Shipping Reliable RAG
Proving it works and keeping it working: eval, tracing, security, routing.
8 posts
15 min readDeep dive
Batch Ingestion Is Why Your RAG Answers Go Stale
Batch re-indexing leaves your RAG index lagging the source, so answers go stale. Stream changes with CDC, upsert by hash, and pin versions to stay fresh.
15 min readDeep dive
Model Routing and Fallbacks Without Silent Degradation
Model routing cuts LLM cost and adds failover — but each request can hit a different model. Pin the context and log the route, or fallbacks hide regressions.
13 min readDeep dive
RAG Prompt Injection: Your Corpus Is the Attack Surface
In RAG, retrieved documents are untrusted input. Indirect prompt injection turns your knowledge base into an attack surface — defend at ingestion and retrieval.
15 min readDeep dive
LLM Observability for RAG: Trace the Regression
You can't fix a RAG regression you can't see. Instrument every request as one trace, watch the right signals, and run online evals to catch it fast.
12 min readDeep dive
LLM Observability: A Trace You Can't Replay Is a Screenshot
Span trees and token counts tell you what your RAG pipeline did. Reproducing the incident needs the corpus state it ran against. Instrument both.
15 min readDeep dive
RAG Evaluation: Prove a Retrieval Change Actually Helped
You can't tell if a retrieval tweak improved RAG by reading a few answers. Measure it in two metric layers, then gate the ship on a paired test.
12 min readDeep dive
KV Caching Is Not Deterministic Retrieval
KV and prefix caching speed up prefill by reusing exact token prefixes. They do not make your RAG answers reproducible — that is a separate layer, upstream.
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