Agents operating on data without anything to help them causes “believable nonsense.” Data quality stops agents from misrepresenting what the warehouse contains but you need context engineering to put the right meaning, rules, and situational information in front of the model at the right moment.More
Tag Archives: prompt engineering
222: Ashley Langford: How senior MOps practitioners are navigating the 2026 job search
Jason breaks down the 5 non-negotiables of minimum viable readiness before you deploy any AI agent, explains why the marketing ops function is becoming more critical as AI takes over execution, and argues that unbounded AI autonomy creates more risk than warehouse data ever will. He also defends GTM engineering as a real discipline rather than a rebrand, and closes with a Dune analogy.More
221: Jason Dobbs: You need Minimum Viable Readiness for AI because perfect data doesn’t exist
Jason breaks down the 5 non-negotiables of minimum viable readiness before you deploy any AI agent, explains why the marketing ops function is becoming more critical as AI takes over execution, and argues that unbounded AI autonomy creates more risk than warehouse data ever will. He also defends GTM engineering as a real discipline rather than a rebrand, and closes with a Dune analogy.More
220: Alex Halliday: How to build content engineering systems that get cited and scale without slop
Alex breaks down what content engineering actually means: building the systems infrastructure to maintain quality, freshness, and brand accuracy across everything a company has ever put online. He makes the counterintuitive case that great content engineering puts more humans into the content process.More
214: Austin Hay: Claude Code is creating a new class of elite marketers and the mental models that make it click
You’ll be hard pressed to find someone that understands martech and is more advanced in their Claude Code journey than Austin Hay. He maps the 2 chasms separating most marketers from big AI leverage, makes the case for a new class of professional he calls the white collar super saiyan, and walks through the automations he’s actually built.More
198: Pam Boiros: 10 Ways to support women and build more inclusive AI
Pam delivers a clear, grounded look at how women learn and lead with AI, moving from biased datasets to late-night practice sessions inside Women Applying AI. She brings sharp examples from real teams, highlights the quiet builders shaping change, and roots her perspective in the resilience she learned from the women in her own family.More
194: Jane Menyo: How Gong democratized customer proof with AI research and standardized prompts
Jane built her marketing practice around listening. At Gong, she turned raw customer conversations into a live feedback system that connects sales calls, product strategy, and messaging in real time. Her team uses AI to surface patterns from the field and feed them back into content that actually reflects how people buy. More
174: Joshua Kanter: A 4-time CMO on the case against data democratization
Joshua spent the earliest parts of his career buried in SQL, only to watch companies hand out dashboards and call it strategy. Teams skim charts to confirm hunches while ignoring what the data actually says. He believes access means nothing without translation. You need people who can turn vague business prompts into clear, interpretable answers.More
170: Keith Jones: OpenAI’s Head of GTM systems on buying martech with cognitive extraction and ghost stories
The best martech buying process isn’t a spreadsheet. It’s a cognitive extraction exercise.
Keith Jones asks stakeholders to write what they want, say it out loud, and then feeds both into GPT to surface what actually matters. That discipline applies to agents too. Most teams chase orchestration before they have stable logic, clean data, or working workflows. More