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Opportune
First cohort · FreeThe context audit

Context engineering.
The diagnostic framework.

A structured course for SDE 1-3s building with LLMs. Learn to diagnose and fix context failures in days — not months of iterating blind.

What your eval suite misses and production catches
How to run a full context audit in 2 days
The 3 root causes behind 80% of production LLM failures
How to fix without changing your model

Register your interest

First cohort is free.

We want to get this into the hands of engineers who need it. No strings attached.

What you'll learn

The context audit — four lessons.

A complete, self-contained method for diagnosing what's in your context window and why it's failing. Four lessons. No prerequisites beyond having a production LLM.

01

What a context window actually contains

Most engineers know what they put in. Few know what arrives. Learn to read the assembled context as the model sees it — retrieved chunks, system instructions, conversation history, all of it.

02

Token attribution — where your budget goes

Attribute every token in the window to its source layer. Find which layers are consuming budget without contributing signal. This is where the cost recovery starts.

03

The signal-to-noise diagnosis

Identify where the model is burying the relevant signal under noise. Learn the three patterns that account for 80% of production failures: bloat, grounding gap, and ordering.

04

Running the audit on your own stack

A repeatable process — not a one-time exercise. Walk through the audit step by step on a real production context window and produce a written diagnosis.

Who this is for

Software engineers building with LLMs.

Not a research course. Not a "getting started with AI" primer. This is for SDE 1–3s who are already shipping LLM features — and running into failures they can't explain or fix fast enough.

RoleSDE 1–3, junior to mid-level AI engineer
StageShipping LLM features — not pre-launch
ProblemFailures you can't trace: wrong outputs, cost overruns, unpredictable behavior
LevelAssumes basic familiarity with APIs and prompting — no ML background needed
About the instructor

"I built Saarthi, a clinical AI for India. The system prompt told the model to be India-specific. It wasn't. The model was answering confidently from memory — the documents were decoration. That failure took us months to fully understand and days to fix once we knew what to look for."

Ramanan

Ramanan

Co-founder, Opportune · PhD candidate in language modeling, IIT Hyderabad

I've since run this diagnostic across clinical, voice, and financial AI stacks. The root cause is almost always the same. This course teaches you to find it.