Context Engineering: Advanced — Memory & Token Budgeting

This course teaches builders to design what goes into the context window to maximize reliability and relevance. It covers context windows, retrieval versus stuffing, memory patterns, summarization, and token budgeting. Learners finish able to get more accurate output by controlling context deliberately.

What this course covers

The course runs across 9 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Demystifying the Context Window — Understand the mechanics of LLM context windows, attention mechanisms, and how context length impacts model performance, latency, and cost.
Token Budgeting and Calculation — Learn to estimate, track, and control token usage programmatically. Implement token counting utilities using libraries like tiktoken.
Retrieval vs. Context Stuffing — Analyze the trade-offs between stuffing large amounts of raw text into a prompt versus implementing targeted retrieval (RAG). Explore 'lost in the middle' phenomena.
Implementing Vector-Based Retrieval — Hands-on exercise setting up a local vector store, embedding documents, and querying relevant snippets to inject into the prompt context.
Stateful Memory Patterns — Explore architectural patterns for maintaining conversation state, including buffer memory, windowed memory, and long-term external storage.
Coding a Dynamic Conversation Memory Buffer — Build a memory manager that dynamically prunes, formats, and appends chat history to stay within strict token constraints.
Context Summarization Strategies — Design recursive summarization pipelines to condense historical context, preserving key semantic details while freeing up active window space.
Advanced Memory: Semantic Search over History — Combine summarization and vector retrieval to build a hybrid memory system that recalls both recent messages and semantically relevant past interactions.
Stress-Testing Context & Memory Systems — Conduct evaluation runs to test your context-engineered pipeline under heavy token loads and evaluate output accuracy and latency.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick. This course maps to Large Language Models (LLMs), Summarizing & Synthesizing, Prompt Engineering, LLM Operations (LLMOps), Tokenization, Retrieval-Augmented Generation (RAG).

Large Language Models (LLMs)
Summarizing & Synthesizing
Prompt Engineering
LLM Operations (LLMOps)
Tokenization
Retrieval-Augmented Generation (RAG)

Who it is for

Builders, power users. The material is pitched at advanced level, and takes roughly 120 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Category: Prompting track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including code lab, guided lab exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a lab assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

All Prompting training courses →
Getting Answers in a Fixed Format: Advanced — Structured-Output Recipes — Recipes for output that machines can consume: format contracts, schema-shaped prompts, and robustness tricks that survive model updates. Learners harden a prompt until its output parses every time.
Step-by-Step Reasoning: Advanced — Chain-of-Thought Recipes — An advanced recipe set for eliciting reliable reasoning: decomposition, explicit steps, and built-in verification. Learners apply the recipes to problems where a single-shot answer fails.
Prompt Libraries for Teams: Advanced — Building & Governing — The organizational layer of prompting: curating, storing, and governing a shared prompt library. Learners stand up a real library structure with contribution standards and an upkeep loop.
Prompt Debugging: Advanced — Iteration & Versioning — When a prompt fails, most people guess; this course teaches diagnosis. Learners isolate failure causes, iterate systematically, and version what works.
Advanced Prompt Engineering — Learners master advanced prompting techniques for reliable, structured results. The course covers few-shot and chain-of-thought, structured output, system prompts and roles, and prompt chaining and templates. They finish able to engineer prompts that perform consistently at scale.
Prompting Proficiency Assessment — Prompting Proficiency: can the person get a usable, correctly formatted answer on the first or second try?
Prompt Power User — Move from basic prompting to reliable, advanced technique
Become an AI Builder — Technical foundation to build AI applications

Frequently asked questions

How long does this course take?

It runs roughly 120 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code CAT-CTX.