--- title: Context-First AI Method | Grove-White & Co. description: "Learn the seven-stage Context-First AI Method for professional teams: prepare useful context, set clear limits, review results and improve each workflow." image: https://grove-white.co/hubfs/context-first-ai-method-explained-grove-white.png --- [Skip to content](https://grove-white.co/about-context-first#main-content) [![Grove-White & Co.](https://grove-white.co/hs-fs/hubfs/artificial-intelligence-implementations-ai-governance-strategy-darragh-grove-white.png?width=1200&height=306&name=artificial-intelligence-implementations-ai-governance-strategy-darragh-grove-white.png)](https://grove-white.co/?hsLang=en) Work with us [Private workshopsTraining for your team](https://grove-white.co/private-workshops?hsLang=en)[One-to-one helpA task, question or next step](https://grove-white.co/private-ai-sessions?hsLang=en)[Workflow advisoryImprove a recurring job](https://grove-white.co/#capability)[Compare ways to work togetherFind the right starting point](https://grove-white.co/#work) [Workshops](https://grove-white.co/#work)[Speaking](https://grove-white.co/speaking?hsLang=en)[Insights](https://grove-white.co/insights?hsLang=en)[About](https://grove-white.co/#about) [Start a conversation](https://grove-white.co/start-a-conversation?hsLang=en)Menu Work with us [Private workshops](https://grove-white.co/private-workshops?hsLang=en)[One-to-one help](https://grove-white.co/private-ai-sessions?hsLang=en)[Workflow advisory](https://grove-white.co/#capability)[Compare ways to work together](https://grove-white.co/#work)[Workshops](https://grove-white.co/#work)[Speaking](https://grove-white.co/speaking?hsLang=en)[Insights](https://grove-white.co/insights?hsLang=en)[About](https://grove-white.co/#about)[Start a conversation](https://grove-white.co/start-a-conversation?hsLang=en) Context-First AI for High-Trust Professionals # The Context-First AI Method By Grove-White & Co. Context-First AI is Grove-White & Co.’s method for building useful AI workflows around the work, the knowledge it needs and the people who check it. We gather the right information, give AI a clear job, review the result and save what we learn. The aim is work that your team can repeat, check and improve. AI prepares. Professionals decide. [See the whole method](https://grove-white.co/about-context-first#method)[Start a conversation](https://grove-white.co/start-a-conversation?hsLang=en) ![AI-first is the wider business direction. Context-First sits within it as a focused method for one useful workflow with the right information, a clear job and professional review.](https://grove-white.co/hubfs/context-first-within-ai-first.svg) Grove-White & Co.’s positioning: a practical method within the wider AI-first direction.Context-First within AI-first · Built for the Grove-White & Co. website using ChatGPT 6.1 Sol. [AI-first and Context-First](https://grove-white.co/about-context-first#ai-first)[Seven-stage method](https://grove-white.co/about-context-first#method)[Worked example](https://grove-white.co/about-context-first#example)[How to start](https://grove-white.co/about-context-first#start) 01 / The relationship ## Where does Context-First AI fit within AI-first? Grove-White & Co.’s Context-First AI Method is a focused method within the wider AI-first business direction. It helps a team build and manage one useful AI workflow, with clear information, task limits and professional review. AI-first means looking at how AI could change the way a business works and serves people. It can include new services, staff training, better tools and changes to whole business processes. Context-First connects an AI-first goal to daily work. A firm might want to use AI across its business. Its first Context-First workflow could help prepare a meeting summary from approved notes, using the firm’s format and a named reviewer. Think of AI-first as the wider plan. Context-First supplies a way to build and manage a useful part of that plan. AI-first can also include governance and human review; Context-First makes those needs clear for each job. **AI-first:** the business direction. **Context-First:** the method for one job. ![Darragh Grove-White presenting beside an AI agent spectrum slide to an audience at CFA Society Victoria.](https://grove-white.co/hs-fs/hubfs/context-first-ai-speaking-cfa-society-victoria-darragh-grove-white.jpg?width=1000&height=750&name=context-first-ai-speaking-cfa-society-victoria-darragh-grove-white.jpg) Darragh Grove-White speaking about AI agents at CFA Society Victoria. 02 / The opportunity ## A new opportunity with Context-First AI Context-First AI offers professional teams a new opportunity: turn work knowledge into a reusable AI workflow with clear steps and human review. The team can keep the sources, instructions and checks that support a recurring job, then refresh the facts for each case. Much of a team’s knowledge lives in people’s heads, past conversations and scattered files. An expert may know which source to trust, what a good answer looks like and when the usual rule will fail. AI needs that guidance to help with the work. The shift is from asking for a fresh answer each time to building a capability you can run again. Here, a capability means a defined job that AI can help perform, with the information and review it needs. A prompt can be part of it. The larger asset is the working method around that prompt. A single task ### One-off AI answer - Give AI the job, approved sources and limits for a single task. - Ask AI to prepare a draft. - Have a person review and correct the answer. A recurring job ### Reusable team capability - Save approved sources, working rules and steps for a recurring Context-First AI job. - Use fresh facts for each case. - Name a person who checks results and approves important decisions. - Use the review findings to improve the saved method. For recurring work, the opportunity is a way of doing the job that your team can keep, check and use again. Each new case still needs current facts and professional review. Grove-White & Co. applies the “new opportunity” idea from Russell Brunson’s *Expert Secrets* to professional AI work: offer a different route to a useful goal. This is a positioning choice, not a claim that we invented a new AI model or that results are guaranteed. 03 / The whole method ## The seven stages of the Context-First AI Method The Context-First AI Method has seven stages: Capture, Structure, Configure, Direct, Supervise, Check and Improve. The stages connect approved source material to work that a professional can review and improve. Before building an AI workflow, choose one job and define a good result. Decide which information is allowed, where the work will go and who can accept it. Each stage leaves something useful for the next. The labels below show typical outputs. A small job can keep these parts in one clear document. ![Context-First AI stages and typical outputs: source map, context brief, working rules, job playbook, review draft, checked result and updated version. Corrections return to the affected part; human judgement and checks apply throughout.](https://grove-white.co/hubfs/context-first-seven-stage-method.svg) 1. [01**Capture**Source map](https://grove-white.co/about-context-first#stage-1) 2. [02**Structure**Context brief](https://grove-white.co/about-context-first#stage-2) 3. [03**Configure**Working rules](https://grove-white.co/about-context-first#stage-3) 4. [04**Direct**Job playbook](https://grove-white.co/about-context-first#stage-4) 5. [05**Supervise**Review draft](https://grove-white.co/about-context-first#stage-5) 6. [06**Check**Checked result](https://grove-white.co/about-context-first#stage-6) 7. [07**Improve**Updated version](https://grove-white.co/about-context-first#stage-7) The Context-First AI lifecycle: Capture → Structure → Configure → Direct → Supervise → Check → Improve. Plan checks early, review during the run and use the result to improve the next run.Seven-stage Context-First AI Method · Built for the Grove-White & Co. website using ChatGPT 6.1 Sol. 1. 01 ### Capture: gather the useful source material The Capture stage of the Context-First Method gathers the source material needed for a defined AI job. Sources may include notes, documents, examples and the rules an expert follows without thinking about them. Record where facts came from and what remains unknown. Use only sources that are relevant and allowed for the job. **Typical output:** A source map: the approved inputs and where they live. 2. 02 ### Structure: make the right context easy to find The Structure stage of the Context-First Method organises approved source material into reusable context for a defined job. Context means the information that matters for the job at hand. Context infrastructure is the organised base that keeps it available. A context pack is a small brief drawn from that base for one job or case. Use enough to do the work, and keep unrelated material out. **Typical output:** A context brief: the relevant facts for this job or case. 3. 03 ### Configure: set the working rules The Configure stage of the Context-First Method sets AI’s role, required output, working rules and limits for a defined job. These saved instructions are called configuration. For a meeting-summary job, the rules can require approved notes, a set summary format, visible gaps and a stop before work moves beyond the agreed task. **Typical output:** Working rules: AI’s role, limits and checking instructions. 4. 04 ### Direct: give the job a repeatable route The Direct stage of the Context-First Method gives an AI job a repeatable guide and workflow. A skill is a reusable guide for doing a task. A workflow is the route from the starting request to the finished result. The guide and workflow specify which sources AI should use, what steps to follow and when a person must review the work. **Typical output:** A job playbook: the steps, examples and review points. 5. 05 ### Supervise: run the work with clear ownership The Supervise stage of the Context-First Method runs a defined AI workflow with a named professional reviewer. Start with a small, suitable case. Keep the professional in charge of important choices. Set clear limits on what AI may read, draft, change or send. Give AI more freedom only when evidence supports that change. **Typical output:** A review draft: work prepared within the agreed limits. 6. 06 ### Check: judge the whole result In the Check stage of the Context-First Method, a professional compares an AI workflow’s result with the agreed quality criteria for the job. Check facts against approved sources, flag missing information and confirm whether the required work is complete. Include the time spent preparing, reviewing and fixing the result. A polished draft can still fail the job. Plan checks early and use them during the work as well. **Typical output:** A checked result: accepted work or a clear record of what failed. 7. 07 ### Improve: fix the right part and test again The Improve stage of the Context-First Method uses review findings to repair the part of an AI workflow that failed. A stale source needs a source update. A missed step needs a workflow change. An unclear instruction needs clearer wording. Save what changed and why, then test the affected work again. Keep useful versions so learning carries forward. **Typical output:** An updated version: the change, its reason and the retest. ![Audience view of Darragh Grove-White speaking on stage at the Advanced Markets Symposium.](https://grove-white.co/hs-fs/hubfs/advanced-markets-symposium-ai-speaker-2026-darragh-1.png?width=1000&height=751&name=advanced-markets-symposium-ai-speaker-2026-darragh-1.png) Darragh Grove-White speaking at the Advanced Markets Symposium. 04 / The management view ## Four questions for checking Context-First AI work A professional can review a Context-First AI workflow through four questions about its information, steps, decision owner and evidence of quality. 01 ### Context Does the AI workflow use approved, current information for the defined job? 02 ### Procedure Does the AI workflow have clear steps, limits and points for human review? 03 ### Judgement Which professional approves the AI-supported result and handles exceptions? 04 ### Evidence What checks show that the AI-supported result is accurate, complete and useful for the agreed job? The four questions form a management view of the Context-First Method. They apply across Capture, Structure, Configure, Direct, Supervise, Check and Improve. Human judgement and review belong throughout the work, including the choice of which job to build. 05 / One worked example ## Example: Context-First AI for a client meeting summary This illustrative client meeting summary shows how Context-First AI keeps missing information visible for professional review. The example is made up to explain the method; it is not a measured client result. Start with permitted notes from the current meeting, the firm’s summary template and its review rules. Keep this client’s facts separate from other cases. AI’s task is to organise the discussion, decisions, actions and open questions using only those sources. ![Illustrative Context-First meeting summary: notes ask for a summary with no deadline. The AI draft marks the deadline missing. A professional confirms Friday with the client before adding the date and approving the summary.](https://grove-white.co/hubfs/context-first-meeting-summary-example.svg) 1. **Illustrative meeting note**In this made-up example, the note says “Send the client a summary of the options we discussed.” No deadline is recorded. 2. **Illustrative AI draft**For a meeting-summary request with no recorded deadline, AI drafts: Action: send summary. Deadline: MISSING. 3. **Illustrative professional review**In this made-up meeting example, the professional confirms the missing date with the client, adds Friday only once agreed and checks the summary before sharing. In this illustrative Context-First meeting-summary workflow, Friday is a new fact confirmed by a person, not inferred by AI.Illustrative Context-First AI meeting summary · Built for the Grove-White & Co. website using ChatGPT 6.1 Sol. ### Illustrative meeting-summary source note In this made-up meeting example, the approved note says: “Send the client a summary of the options we discussed.” The note records no deadline for sending the summary. ### Illustrative meeting-summary AI draft In this illustrative Context-First workflow, AI drafts a meeting follow-up from approved notes that contain no due date. **Action:** Send the summary. **Deadline:** Missing. A professional must confirm a date before adding one. ### Illustrative meeting-summary professional review In this made-up example, a meeting note asks for a summary but gives no deadline. The professional asks the client to confirm a date. They agree on Friday. The reviewer adds “Friday, confirmed with the client” and checks the summary against the meeting notes before sharing it. If AI guesses a deadline, fix the instruction that allowed the guess and test another set of incomplete notes. The reusable part of this meeting-summary workflow is the method. The changing part is the current meeting’s facts. Saving the method does not remove the need to refresh the case facts. 06 / Related concepts ## How does Context-First AI relate to prompt and context engineering? Context-First AI uses prompt and context engineering as parts of a wider professional workflow. Prompt engineering improves the instructions given to AI. Context engineering deals with the information and tools available to the model when it works. Grove-White & Co.’s Context-First Method also covers the wider job: choosing sources, saving useful knowledge, setting permission limits, deciding who reviews the work and learning from real results. Better prompts and better context support the workflow. Professional judgement determines whether the result deserves to be used. 07 / Who it helps ## Who is Context-First AI for? Context-First AI is designed for high-trust professional work, where facts, client needs and review matter. Examples include insurance, wealth, financial planning, consulting and other professional services. Useful starting jobs include meeting summaries, internal briefings, case preparation, policy reference and team handoffs. Each needs its own sources, task limits and review. The level of control should match the risk. Clearer context can help, but the method does not guarantee accuracy, safety or compliance. ![Darragh Grove-White teaching an AI workshop beside a screen while participants work at laptops at the Union Club of British Columbia.](https://grove-white.co/hs-fs/hubfs/context-first-ai-workshop-union-club-darragh-grove-white.jpg?width=1000&height=750&name=context-first-ai-workshop-union-club-darragh-grove-white.jpg) Darragh Grove-White leading an AI workshop at the Union Club of British Columbia. 08 / Your next step ## How can your team start with Context-First AI? Start Context-First AI with one recurring job, approved inputs, a defined output and a named professional reviewer. Define what finished means. Test a small case using an approved tool. Review what happened before expanding the scope. ### Work with Darragh Grove-White Darragh Grove-White is the founder of Grove-White & Co., based in Victoria, British Columbia. He leads practical AI training, speaking and consulting for high-trust professionals. His work focuses on the knowledge a job needs, how it moves between people and tools, and who reviews the result. [Meet Darragh](https://grove-white.co/?hsLang=en#about) Grove-White & Co. helps teams apply Context-First AI through education, workflow design, implementation and managed capabilities. A keynote can give an audience shared language. A private workshop can help a team practise on relevant work. Advisory support can help put a defined workflow into use and maintain it. Bring one job your team wants to improve. We can discuss the information it needs, the review it requires and the right next step. [Start a conversation](https://grove-white.co/start-a-conversation?hsLang=en) · [Explore private workshops](https://grove-white.co/private-workshops?hsLang=en) · [Explore speaking](https://grove-white.co/speaking?hsLang=en) 09 / Common questions ## Common questions about the Context-First AI Method ### What is the Context-First AI Method? The Context-First AI Method is Grove-White & Co.’s way to build an AI workflow around a real job. It brings together the right sources, clear working rules and a person who reviews the result. The team saves useful lessons so the next run can improve. ### How does Context-First AI fit within an AI-first business? Context-First AI is a working method within the wider AI-first business direction. AI-first looks at how AI could change a business. Context-First helps a team build and manage one useful part of that plan, with clear sources, task limits and review. ### What are the seven stages of the Context-First Method? The seven Context-First stages are Capture, Structure, Configure, Direct, Supervise, Check and Improve. They connect approved source material to work a person can review. Each stage leaves a useful output, such as a source map, working rules, a checked result or an updated version. ### How do you start a Context-First AI workflow? Start a Context-First AI workflow with one recurring job. Define the result, choose approved sources and name a reviewer. Set limits on what AI may do. Test a small case in an approved tool, then review the work before giving AI a wider role. ### What belongs in a Context-First context pack? A Context-First context pack holds the information needed for one job or case. It can include current facts, source links, a useful example, the required format and known gaps. Keep case facts separate from other clients. Leave out information that is unrelated or not allowed. ### What is the difference between context and a prompt? In the Context-First Method, context is the information that matters for the job. A prompt tells AI what to do with that information. A good prompt helps, but it cannot supply missing facts, refresh stale sources or decide who should approve the result. ### What working rules does a Context-First AI agent need? A Context-First AI agent needs a defined role, approved sources, a required output and clear limits. Its rules should say how to show missing facts, when to ask a person and when to stop. Reading, drafting, changing records and sending work need their own limits. ### How does feedback improve the Context-First system? Feedback in the Context-First system returns to the part that needs repair. A stale fact needs a source update. A missed step needs a workflow change. A guessed detail may need a clearer rule. Save the change and its reason, then test the affected work again. ### How much freedom should a Context-First AI agent have? A Context-First AI agent should have only the access and freedom needed for its current job. Start with a small task and review its work. Give it more freedom only when checks support that change. Success on one task does not prove it can handle a different task. ### Where can teams use the Context-First Method? Teams can use the Context-First Method for recurring work such as meeting summaries, internal briefings, case preparation, policy reference and team handoffs. Choose work with clear sources and a result that a person can check. Match the level of control to the risk of the job. ### Does Context-First AI require a custom AI tool? Context-First AI does not require a custom tool. Start with the tools your team is allowed to use. A custom tool may help a specific job later. The Context-First Method is separate from any one platform. ### Does more context always improve AI work? More context does not always improve AI work. Old, conflicting or unrelated information can make a result worse. Use the right information for the current task and keep its source clear. ### Can Context-First AI replace professional judgement? Context-First AI can help prepare, organise and compare work. A professional remains responsible for important decisions and approval. More automation needs its own evidence and clear limits. ### What should stay under human control in a Context-First workflow? In a Context-First workflow, people set the goal, choose allowed sources, define quality and decide who can approve the work. People also handle important choices and exceptions. A workflow should name these review points before AI runs, including any approval needed before work is shared. **Sources and framing.** Darragh Grove-White developed this seven-stage adaptation for professional work, building on Eugene Yan’s work. Yan’s [How to Work and Compound with AI](https://eugeneyan.com/writing/working-with-ai/) discusses context, working rules, checks, delegation and learning from feedback. Our AI-first description relates to the wider business direction discussed by [Adam Brotman and Andy Sack](https://www.forum3.com/book). The new-opportunity framing is our application of Russell Brunson’s *Expert Secrets* idea, which he discusses in [his opportunity-marketing explanation](https://www.innercircleforlife.com/blog/518-what-s-the-new-opportunity-inside-of-your-current-opportunity). These references do not imply endorsement. [![Grove-White & Co.](https://grove-white.co/hs-fs/hubfs/grove-white-ai-consulting-professional-services-implementations-logo-white.png?width=1200&height=306&name=grove-white-ai-consulting-professional-services-implementations-logo-white.png)](https://grove-white.co/?hsLang=en) Context-First AI for High-Trust Professionals. Victoria, British Columbia ## Work with us - [Private workshops](https://grove-white.co/private-workshops?hsLang=en) - [One-to-one help](https://grove-white.co/private-ai-sessions?hsLang=en) - [Workflow advisory](https://grove-white.co/#capability) - [Private intensives](https://grove-white.co/#work) ## Explore - [Workshops](https://grove-white.co/#work) - [Speaking](https://grove-white.co/speaking?hsLang=en) - [Insights](https://grove-white.co/insights?hsLang=en) - [About](https://grove-white.co/#about) - [Context-First Method](https://grove-white.co/about-context-first?hsLang=en) ## Your next step - [Start a conversation](https://grove-white.co/start-a-conversation?hsLang=en) - [AI for insurance advisors](https://grove-white.co/insurance-advisors?hsLang=en) - [Get email updates](https://grove-white.co/email-updates?hsLang=en) © 2026 Grove-White & Co. AI prepares. Professionals decide. 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