Your prompt is a sliver of what the model actually reads — the rest decides whether the answer is right, fast, and affordable.
Your assistant answered correctly yesterday and wrong today. Costs tripled the week you added document search. It forgets what the user told it four turns ago. None of those are prompt problems — they're context problems: what a large language model can actually see when it generates the next token, and in what order. This course treats that window as a budget you engineer. You'll count real tokens instead of guessing at them, measure where in the window a fact quietly gets ignored, and build a selector that picks what fits. Then you'll add retrieval that ranks and cites its sources, a three-tier memory that decides what to keep and what to forget, bounded tool results, compression you can prove kept the facts, and prompt caching that cuts the cost of repeated prefixes. The final module is measurement: retrieval and generation metrics, using one model to grade another without fooling yourself, ablations that prove each piece earns its tokens, and a continuous-integration gate that blocks quality regressions. Python throughout, running against Anthropic and OpenAI.
Built by Lakshya Kumar
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Sign in to applyFinished the tasks? Take the prompt to your AI and get tested on it. We copy the prompt and open the app — just paste it in.
The model reads nothing the way you wrote it. See how roles, token counts, and position quietly change the answer.
Dumping in everything you have makes answers worse, not safer. Pick under a budget, drop duplicates, and know when to say no.
Search that returns roughly-related text sinks the answer. Chunk, rank, and cite so the model gets passages it can use.
An assistant that recalls everything is as broken as one that recalls nothing. Decide what's worth keeping, and what to drop.
One tool call can dump 40,000 tokens into the window — and whatever it returns might be hostile. Bound it, shape it, distrust it.
Every long session eventually outgrows its window. Summarize and prune on a trigger, then prove the facts survived.
You're paying full price to resend the same system prompt thousands of times a day. Order it right and the provider stops charging.
A huge window doesn't mean you should fill it. Test what your model really recalls, then price the answer against retrieval.
Without numbers, every context change is a guess that felt right. Score retrieval and answers, then block regressions before they ship.
Complete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Build a complete context pipeline for a real corpus and use case: gather → score → select-under-budget → order → format, with dedup, recency, retrieval + rerank, three-tier memory, compression with fidelity guarantees, and a layered cache. Ship it behind an API and prove quality with an eval harness (retrieval + generation metrics) and a CI regression gate. Submit the repo, the eval report, and a cost/latency breakdown.
Paste this into any AI chat. Fill in the bracketed parts with your context — you'll get back a straight answer on whether this belongs on your plate.
I'm taking a "Context Engineering" course — engineering the LLM context window as a budgeted resource: what to include, in what order, how to compress and cache it, and how to measure it. It covers the anatomy of the window (roles, tokens, position effects), retrieval as selection, three-tier memory, tool results, compression, prompt + semantic caching, long-context strategy, and evals/regression gates. Python-first against Anthropic + OpenAI. Here's my context: 1. What I'm building: [describe the feature/product] 2. My current context approach: [naive concatenation / basic RAG / agent / long-context stuffing] 3. Where it's failing: [inconsistent answers / too expensive / too slow / hallucinations / forgets things] 4. My model + window: [model and context size] Given that, answer: - Which module should I prioritize and why? - Which of the five levers (select / order / compress / cache / measure) is most likely my bottleneck? - Name 3 concrete changes I could make this week, and how I'd measure that each one helped. - Name 1 thing this course won't fix so I have the right expectations.
Build a reusable eval pack another team could drop onto their RAG/agent: labeled-set tooling, retrieval + generation metrics, a calibrated LLM-as-judge, ablation runner, online-signal ingester, and a CI gate. Submit it as a small package or repo with docs.
Ship a memory system (short-term + summarized + long-term) with salience extraction, relevance-gated recall, PII redaction, and cross-session continuity, integrated into a working assistant. Submit the live demo + a writeup of what it remembers and forgets and why.
Build prompt caching + semantic caching + embedding caching for a real workload with version-based invalidation, then report measured hit rate, cost savings, latency reduction, and false-hit rate over real traffic. Submit the implementation + the metrics dashboard.
Pick a real corpus and produce a rigorous decision report: needle-in-haystack recall map for your model, long-context and retrieval (and hybrid) implementations, and an accuracy/latency/cost comparison ending in a defensible recommendation. Submit the benchmark code + report.
The position-effects paper behind Modules 2 and 9. Required reading.