Context engineering
In short
Context engineering is the discipline of designing the information an AI model sees when it runs: instructions, data, examples, tool results, and memory. The goal is to give the model exactly what it needs to do the task well, and nothing that distracts it.
It goes beyond writing a good prompt. A prompt is one piece of context. Context engineering also covers what data is retrieved, how it is formatted, and how it is kept current across a multi-step workflow.
What goes into context
- Instructions: The role, rules, tone, and output format.
- Task data: Verified account, contact, and signal records relevant to this request.
- Examples: A few samples of good output.
- Tool results: Information returned from searches, the CRM, or other systems.
- History: Earlier steps or conversation that still matter.
Core practices
Retrieve only what is relevant
Too much text dilutes the important parts and raises cost. Pull the records that apply to the specific account and question.
Structure the data
Label fields clearly, such as "employee count: 450" and "last funding: unknown". Explicit gaps stop the model from guessing.
Keep it current
Stale context produces stale answers. Refresh data at run time instead of pasting old snapshots.
Test and measure
Run a fixed set of cases after every change and compare outputs.
Example
Say a research agent writes account briefs. At first it receives only the company name and returns generic text. The team changes it to receive the firm record, three recent signals, the ICP definition, and a sample brief, with a rule to write "not found" for missing fields. Brief accuracy improves because the model now works from supplied facts.
Context engineering vs. prompt engineering
Prompt engineering focuses on the wording of instructions. Context engineering manages the whole input, including data and tools, across a system.
Common mistakes
- Dumping everything: More text is not better context.
- Unlabeled data: Raw exports leave the model to interpret fields.
- No gaps shown: Missing values should be stated, not hidden.
Context engineering FAQs
What is context engineering?
Context engineering is the practice of designing what information an AI model receives, including instructions, retrieved data, examples, and tool outputs, so it can complete a task accurately. It treats the whole input, not just the prompt, as something to design.
How is context engineering different from prompt engineering?
Prompt engineering focuses on how instructions are worded. Context engineering is broader: it decides which data is retrieved, how it is structured, what history is kept, and which tools the model can use, across every step of a workflow.
Why does context engineering matter for AI sales agents?
An agent can only act on what it is given. Supplying verified account data, relevant signals, and clear rules makes outputs more accurate and reduces invented facts, while missing or stale context leads to generic or wrong messages.
How do you start with context engineering?
Pick one workflow, list what the model needs to do it, connect trusted data, label fields clearly, mark missing values explicitly, and build a fixed test set. Change one element at a time and compare results before expanding.
