AI Context Bank Workflow
Ground AI in source material and exemplars before asking it to draft or critique.
- Difficulty
- Moderate
- Time to result
- ~days to results
- Steps
- 6
- Confidence
- 95%
The AI Context Bank Workflow treats a language model as a context-sensitive research and writing assistant. Assemble a bounded source bank such as customer-call transcripts, technical material, previous advertisements, or exemplary writing. Ask the model to identify recurring language, problems, desired outcomes, and the mechanisms that make the exemplars effective. Only then request headlines, copy, interview questions, explanations, or critiques grounded in that material. The system can also simplify unfamiliar concepts and create a baseline set of questions that the human improves. Before shipping, compare every important claim with the source bank and apply human editorial judgment. The mechanism works because relevant context and analyzed examples constrain the model more effectively than an isolated request for good content.
Origin
Extracted from Marketing Against The Grain as the hosts and Steph Smith compared practical uses of AI in research, customer-language analysis, copy generation, and critique.
Core principles
- 01Use AI as an assistant rather than an unquestioned author.
- 02Supply source evidence before requesting synthesis.
- 03Exemplars teach the system what good output should accomplish.
- 04Critique and explanation can be as valuable as generation.
- 05Human judgment remains responsible for the shipped result.
How to run it
- 1
Define the Output Decision
Specify whether AI should explain, synthesize, draft, brainstorm, or critique. Identify what a useful result must enable the human to decide or create.
Pro tip A narrow role produces more evaluable output than a request to handle the entire project.
Watch out Do not confuse fluent output with a completed decision.
- 2
Build the Source Bank
Gather relevant transcripts, documents, examples, and constraints. Remove irrelevant material and organize sources so important distinctions remain visible.
Pro tip Include direct customer language when writing customer-facing copy.
Watch out Sensitive or unauthorized data should not be uploaded to an external model.
- 3
Analyze the Evidence
Ask AI to find recurring phrases, problems, desired outcomes, objections, and patterns. Require the analysis to remain traceable to the supplied material.
Pro tip Request contrasting patterns as well as commonalities.
Watch out A model may overstate patterns that appear in only one source.
- 4
Teach Through Exemplars
Provide strong prior examples and analyze why they work before requesting analogous output. Distinguish the underlying mechanism from superficial style.
Pro tip Explain the benefit, evidence, and structure behind each exemplar.
Watch out Style imitation without factual grounding produces polished but weak copy.
- 5
Generate and Critique
Request candidate outputs, then ask the model to identify weak hooks, unsupported claims, unclear logic, and likely objections. Improve the candidates rather than accepting the first response.
Pro tip Use separate generation and critique passes.
Watch out Self-critique does not replace independent fact-checking.
- 6
Verify and Ship
Check claims, quotations, and customer language against the source bank. Edit for judgment, accuracy, brand voice, and ethical use before publication.
Pro tip Retain links between final claims and their supporting sources.
Watch out Never publish invented customer evidence or unattributed model assumptions.
In the wild
A marketer uploads 20 customer-call transcripts, asks AI to identify recurring phrases and the largest scaling problems, and then uses those findings to redraft a homepage in a proven advertising structure.
→ The resulting copy reflects actual customer language and specific desired outcomes rather than generic marketing claims.
A host asks AI to explain quantum mechanics at an elementary level and generate initial interview questions. The generic questions establish a baseline that the host then improves through personal research and judgment.
→ The host reaches sufficient understanding to ask clearer, more differentiated questions.
Common mistakes
Prompting Without Context
A standalone request for strong copy or questions tends to produce generic material disconnected from customer evidence.
Shipping the First Draft
AI output should be checked against sources and edited by a human rather than treated as automatically publishable.
Copying Surface Style
Imitating an exemplar's tone without understanding its underlying benefit and evidence misses what made it effective.
Is it for you?
Best for
Marketers and creators using AI to research, draft, improve, or evaluate audience-facing content.
Not ideal for
Tasks requiring authoritative facts when the source bank is incomplete, unreliable, or cannot be independently verified.
From the transcript
“if you give it context beforehand, we've talked about this in the show before. Yeah. Whatever that training data is, like that little bit of…”
“I've also used it to your point as a feedback partner.”
From the episode
The Secret Growth Strategies Behind Spotify's Top Podcasts