Context Engineering Consulting
Design the context layer production agents trust — retrieval, tools, permissions, and budgets — not a longer system prompt in ChatGPT Enterprise.
- Service
- Implementation
- Industry
- Enterprise
- Updated
- 2026-08-25
- Engagement
- 4 wks
Context engineering consulting designs the layer an LLM actually sees: permissioned retrieval, tool results, memory policy, and a token budget — versioned and evaluated in your stack. It is not prompt-engineering theater inside ChatGPT Enterprise or Microsoft Copilot. Custom GPTs are prototypes; production agents need this layer, typically installed on one path in a four-week standard. You own the specs and evals; you pay the model provider with no markup; SSO/IdP stays yours.
Why teams pick this engagement
Implementation × EnterprisePermissions in the context window
What the model sees is filtered by the asking user’s live IdP groups. Context engineering that dumps SharePoint into the prompt is just Copilot oversharing with extra steps.
Budgeted tokens, measured recall
Every extra chunk has a recall and a cost. We set a context budget and prove retrieval hit rate on a golden set before we “add more context.”
Tools are context too
Tool schemas, allowlists, and write-action confirmations sit beside retrieved text. Custom GPTs that cannot call tools safely stay prototypes.
Domain owners write the truth
Policy, product, and ops owners label what is canonical. Engineers pack it. Context engineering fails when IT guesses which PDF is current.
Four-week standard
Week 1 sources and permissions, week 2 packing design, week 3 build and traces, week 4 evals and handover. One production path, not every corpus.
Reconstructable packing
Traces show which chunks, tool results, and memories entered the window. That is AI observability on context, not a prompt graveyard.
Key takeaways
- 01
Context engineering is the packing of retrieval, tools, permissions, and memory into a budgeted window — not a 4,000-word system prompt.
- 02
If context is not filtered by live IdP entitlements, you have built a leak with citations.
- 03
Copilot is often the wrong tool when write-actions and evals are required; Custom GPTs cannot version this layer.
- 04
Every packing change is an LLMOps change: golden-set recall and faithfulness must pass in CI.
- 05
Four weeks covers one production path you own; expanding corpora reuses the same pack spec.
What the engagement covers
How we work
- 01
Discover
Week 1: corpora, ACLs, current prompts/GPTs, failure modes (wrong doc, empty retrieval, oversharing), cost per call.
- 02
Design
Pack spec, token budget, tool slots, eval slices, and the written limit of Custom GPTs vs this layer.
- 03
Build
Week 2–3: retrieval, packing code, traces, IdP-aware filters in your cloud.
- 04
Validate
Golden-set recall and faithfulness, permission tests, token-budget compliance, red-team of prompt injection via retrieved text.
- 05
Enable
Week 4: spec, owners, CI evals, 30 days on-call. You own context engineering; we do not host the index as a product.
Take the playbook with you
The working documents from real engagements — free, in exchange for an email. They’re useful whether or not we ever talk.
Context Pack Spec Template
The fields we version in git: sources, ACLs, chunking, token budget, tool results, memory policy, refusal-on-empty.
Get the spec ·Retrieval and Context Eval Sheet
Hit rate, faithfulness, citation, and token-per-answer — the slices that decide whether context engineering worked.
Get the sheet ·