Open the code. Find the risks. Write the review.
Most AI apps look fine in demos. The real problems hide in the code path — request handling, session state, tool execution, retrieval quality, retries, observability, cost tracking, and deployment assumptions. This accelerator teaches you how to review AI codebases systematically, identify what can break, and write clear engineering review comments before the system reaches production.
✓ Pre-release access · ✓ Review labs · ✓ Checklists · ✓ Rubrics · ✓ Lifetime updates
The code runs. Tests pass. The demo looks clean. And then production traffic, real users, and live cost meters expose every assumption that nobody reviewed.
The API responds locally, but long-running LLM calls, missing timeouts, and synchronous execution can collapse under real traffic — latency spikes, exhausted workers, timeout cascades.
Direct tool access may look impressive in demos, but production systems need policy checks, audit logs, approval boundaries, and retry safety — otherwise the agent is a liability surface.
Without evals, metadata controls, document freshness checks, and access filtering, a RAG system can silently return the wrong context — and nobody notices until a customer does.
In most courses, you start with a blank file and build a demo. In this accelerator, you enter like a reviewer.
You look at existing AI system patterns. You inspect the code path. You mark risks. You explain why they matter. You decide what must be fixed before production.
Nine concrete review surfaces. Each card answers one question: what exactly will you inspect in the code path?
Inspect: endpoint contracts, LLM call boundaries, sync vs async paths, timeout handling, background-job hand-offs, response shapes.
Inspect: user_id / session_id scoping, global-state leaks, Redis TTLs, persistence boundaries, crash recovery, multi-user isolation.
Inspect: chunking strategy, metadata filtering, reranking, document freshness, retrieval evals, tenant access control.
Inspect: tool allowlists, gateway patterns, policy checks, approval boundaries, audit logs, unsafe direct-call paths.
Inspect: retry wrappers, duplicate side-effects, idempotency keys, partial-failure recovery, dedupe boundaries, queue safety.
Inspect: trace IDs, structured logs, latency capture per stage, token / cost attribution, failure classification, debuggability gaps.
Inspect: prompt-injection surface, secret handling, data exfiltration paths, PII boundaries, permission scoping, audit trails.
Inspect: environment config, Docker hygiene, health checks, queue / worker separation, rate limits, scaling assumptions.
Inspect: how to convert a vague worry into a specific, defensible review comment — with risk, fix, and reasoning in plain language.
Each module is a focused review workshop. You don't watch concepts — you inspect code patterns and leave with a concrete review artefact. Released progressively from 10 June 2026.
Goal: Learn how to review AI systems beyond "it works." Set the bar: open the code, walk the path, mark the risks, write the review.
Goal: Inspect FastAPI endpoints, LLM call boundaries, timeouts, async patterns, and background job needs. Identify exactly where the request path fails under load.
Goal: Inspect user_id / session_id handling, global state risks, Redis TTL, persistence, and crash recovery. Spot the leaks before users do.
Goal: Inspect chunking, metadata, retrieval strategy, reranking, stale documents, access control, and eval gaps. Catch silent retrieval drift before it ships.
Goal: Inspect tool access, gateway patterns, policy checks, retry behavior, audit logs, and unsafe execution paths. Identify every ungoverned-call surface.
Goal: Inspect duplicate calls, unsafe retries, partial failures, queue boundaries, and recovery logic. Find the path where one timeout becomes two charges.
Goal: Inspect trace IDs, structured logs, token / cost capture, latency visibility, and failure classification. Catch the systems nobody can debug after the fact.
Goal: Convert findings into clear code review comments, architecture feedback, and interview-ready explanations. Move from vague concern to specific engineering finding.
You will not just watch concepts. You will inspect broken or incomplete production-style patterns and learn how to review them — risk by risk, comment by comment.
Find long-running LLM calls inside request-response paths. Walk the failure modes — gateway timeouts, worker exhaustion, cascading retries — and write the review.
Find unsafe state handling and missing user / session isolation. Track exactly how one user's context can land in another user's reply, and where the boundary should have been.
Find where retries can create duplicate LLM / tool execution. Identify the missing idempotency key, the right layer to enforce it, and what to recommend in the review.
Find missing evals, weak metadata control, and stale document risks. Show how the system would silently return the wrong context — and what evidence the team should have shipped.
Find missing policy checks, audit logs, and approval boundaries. Map the unsafe execution surface and write the gateway-pattern recommendation the codebase needs.
Find missing trace IDs, token capture, latency logging, and cost tracking. Identify what cannot be debugged today and the first three pieces of instrumentation to add.
Find hardcoded config, missing health checks, and weak runtime assumptions. Surface every deployment assumption that breaks the moment this leaves localhost.
The difference between a junior worry and a senior finding is specificity. Three examples of the exact shift this accelerator teaches.
“This API may not scale.”
The endpoint performs a long-running LLM call inside the synchronous request path. Under concurrent traffic, this can increase latency, exhaust workers, and create timeout failures. This should move behind a job queue or async execution boundary with status polling.
“Memory handling is risky.”
Conversation state is not isolated by user_id and session_id. In a multi-user environment, this can cause data leakage or context contamination. State access should be scoped per user / session with TTL and persistence boundaries.
“Tool calling needs governance.”
The agent can invoke tools directly without policy validation, approval rules, or audit logs. Production tool execution should go through a gateway that enforces permissions, validates inputs, records execution, and handles failure safely.
Concrete review artefacts you carry into your own codebase, your own pull requests, and your own interview rounds.
The grading sheet — every surface, scored by severity and readiness.
The master pre-ship list across reliability, governance, observability, ops.
Request flow, LLM call boundaries, timeouts, async patterns, background hand-offs.
A structured write-up format for chunking, retrieval, evals, freshness, and access.
Per-tool risk pass — gateways, policies, audit trails, side-effect safety.
Duplicate calls, idempotency keys, partial-failure recovery, queue dedupe.
Template that captures missing trace IDs, cost capture, latency visibility.
Config, Docker, health checks, queues, rate limits, scaling assumptions.
Ready-to-adapt phrasings for risk, fix, and reasoning — no PR-blocking tone wars.
How to narrate findings clearly in interviews and engineering discussions.
Premium accelerator. Built for working engineers and senior learners who want sharper judgment under real-world complexity — not absolute beginners or shortcut-seekers.
Software engineers shipping AI-powered features
Backend, DevOps, and MLOps engineers moving into GenAI
Data and ML engineers reviewing production AI codebases
AI/GenAI engineers tightening their architecture judgment
Working professionals preparing for AI/GenAI interviews
Bootcamp and accelerator learners wanting deeper review skills
This is pre-release access. Initial review modules and checklists release first — starting 10 June 2026. Additional labs, examples, and review walkthroughs roll out progressively. Early learners receive all future updates to this accelerator.
🔒 Premium accelerator · No refunds once access is provisioned. Please review the course scope before enrolling.
AI System Design helps you design systems before implementation. AI Code Review Playbook helps you inspect implementation and catch risks after the first version exists. Together with Interview Playbook, Production-Style RAG, and NCP-AAI Prep, they form one path.
If you want to grow beyond building demos, learn how to inspect AI systems the way production teams do — by finding risks in the code path before users, costs, and failures expose them.