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Prompt engineering is about writing the instruction. Context engineering is about curating everything else the model sees when it answers. Anthropic defines context engineering as the set of strategies for curating and maintaining the optimal set of tokens during LLM inference, which in go-to-market terms means deciding which resolved account facts reach the model at the moment it decides. RevSure treats that as a data problem, because the prompt can be perfect and the answer still wrong.
Both practices are real and they operate at different scopes. Anthropic describes prompt engineering as methods for writing and organizing model instructions for optimal outcomes, and draws the contrast in timing: writing a prompt is a discrete task, while context engineering is iterative, with the curation step happening every time you decide what to pass to the model.
What each one covers
Prompt engineering shapes the instruction: the role, the task, the tone, the output format, the examples, the refusals. It is a craft, and it still matters. A well-specified prompt is the difference between an agent that returns a clean JSON object and one that returns a paragraph of apology.
Context engineering shapes the evidence: which records, which history, which definitions, which retrieved passages, and in what order, get placed in front of the model for this specific decision. In a revenue setting that means choosing, out of everything known about an account, the handful of facts that actually bear on whether it is at risk.
Why context becomes the constraint as agents scale
The naive instinct is to give the model everything. Dump the account record, the last forty activities, every transcript, the whole campaign history, and let it sort things out.
That fails for a reason worth understanding. Anthropic's engineering team argues that context must be treated as a finite resource with diminishing marginal returns, that models operate against a limited attention budget when parsing large volumes of context, and that accuracy degrades as the window fills, an effect they name context rot. The goal they describe is finding the smallest possible set of high-signal tokens that produce the outcome you want.
For GTM that reframes the work. The question stops being how to phrase the request and becomes which twenty facts about this account deserve the model's attention. You cannot answer that until the underlying records are resolved, because you cannot select the highest-signal facts from data where the same buyer appears three times and "qualified" means four different things.
Just-in-time retrieval needs something worth retrieving
Anthropic also describes a just-in-time pattern: rather than pre-loading everything, the agent holds lightweight identifiers such as file paths or queries and loads data at runtime through tools.
That pattern transfers cleanly to revenue, with one condition. The identifier has to point at something resolved. An account ID that resolves to one connected view of the buyer works. An account ID that resolves to five conflicting records in five systems just moves the reconciliation problem to inference time, where it competes with the reasoning for the same attention budget.
Techniques for long-running agents
For work that outlives a single context window, Anthropic describes compaction, where a conversation nearing the limit is summarized and restarted from the summary, structured note-taking, where the agent writes durable notes outside the window, and sub-agent architectures, where focused agents work in clean windows and return distilled results.
Revenue work adds a fourth requirement that is less about the model and more about the room. Decisions need traces. When an agent flags a deal or credits a channel, someone will ask how it got there, and an answer that cannot be inspected loses the argument regardless of whether it was right.
A concrete example from a pipeline review
Take a question a CRO asks every week: which deals in this quarter are actually at risk.
The prompt-engineering answer is to write a careful instruction. Define risk, specify the output table, give two examples of a risky deal and one of a healthy one, ask for reasoning. That instruction is worth writing, and on its own it will produce a well-formatted list of the wrong deals.
The context-engineering answer starts a layer down. It asks which buyer is which after deduplication across the CRM and marketing automation, whether this stage means what the neighbouring team thinks it means, how long comparable deals sat at this stage before they closed, which stakeholders have gone quiet and for how long, and what happened after the last pricing conversation. Once those are resolved, the model needs a short, current, high-signal slice of them rather than the raw history.
Same model, same prompt, different answer. That gap is the entire argument.
Where prompt engineering still earns its keep
None of this retires prompt work. Output schemas, tone that matches how your company writes, explicit refusal rules for actions that need approval, few-shot examples for a tricky classification: all of that is prompt engineering, and skipping it produces agents that are correct and unusable.
The practical division is that prompt engineering governs how the agent behaves, and context engineering governs what the agent knows. Bad prompting produces a badly behaved agent. Bad context produces a confidently wrong one, which is more expensive in a revenue workflow because it reaches a customer.
What this looks like at RevSure
RevSure's answer to the context half is structural. Signals from CRM, marketing automation, intent, enrichment, and sales activity are resolved through identity resolution and data harmonization into the Full Funnel Data Graph, then served to agents as current, traceable state. The full method is in RevSure's piece on context engineering for GTM, and the protocol that carries it to external assistants is MCP for GTM.
In Gartner's May 2026 CSO survey, 31% of chief sales officers named difficulty proving the ROI of AI-driven tools as a top challenge for 2026. Prompt tuning does not close that gap. Tracing an outcome back through the accounts and actions that produced it does, and that is context work.
FAQs
What is the difference between context engineering and prompt engineering?
Prompt engineering is writing and organizing the instruction given to a model. Context engineering is curating everything else the model sees at inference: which records, history, definitions, and retrieved passages reach it. Prompting is a discrete authoring task; context engineering is iterative and happens every time you decide what to pass.
Is context engineering replacing prompt engineering?
No. They govern different things. Prompt engineering governs how an agent behaves, including output format, tone, and refusal rules. Context engineering governs what the agent knows. Poor prompting yields a badly behaved agent, while poor context yields a confidently wrong one, which is costlier when the agent acts.
Why not just put all the data in the prompt?
Because context is a finite resource with diminishing returns. Models work against a limited attention budget, and accuracy degrades as the context window fills, an effect described as context rot. The goal is the smallest set of high-signal tokens for the decision, which requires resolved data to select from.
What does context engineering mean for a GTM team specifically?
It means deciding which facts about an account actually bear on the decision, and making sure those facts are resolved before they reach the model. That depends on identity resolution across systems, harmonized definitions of stages and channels, and history kept in time so the agent sees which way the account is moving rather than a single frozen snapshot.
How does RevSure do context engineering?
RevSure resolves and harmonizes signals from CRM, marketing automation, intent, enrichment, and sales activity into the Full Funnel Data Graph, keeps temporal state and decision traces, and serves that resolved context to agents and models, including external assistants connected through its MCP server.