Atiq Israk ships AI products at Chromatics (Kaizen, Anne, Pico) and previously built Navbot (85% inquiry automation). This guide explains what PMs should put in AI memory—context engineering—not how many tools they use.

Key Takeaways

• Context engineering is the PM job of deciding what the model sees—docs, tickets, schemas, policies—not writing the retrieval code.
• Bad context causes hallucinations; good context narrows scope so eval pass rates climb without bigger models.
• Every AI bet needs a context map: sources, freshness rules, and what must never enter the prompt.

What Is Context Engineering for Product Managers?

Context engineering is designing the information environment around an AI feature: which documents get retrieved, in what order, with what metadata, and what stays out for privacy or accuracy. Institute PM's 2026 skills checklist treats RAG and grounding as core AI PM literacy—roughly 8% of US AI product job postings mention RAG explicitly (Axial Search, 12,397 postings analyzed).

PMs own the what and why. Engineering owns chunking, embeddings, and latency. When those blur, you get demo-grade copilots that fail on real menus, SKUs, or policy edge cases.

Why Does Context Matter More Than the Model?

On Kaizen, brand-voice AI improved when we fed structured examples—not when we swapped model tiers. On Navbot, automation hit 85% of inquiries only after context was limited to verified FAQ blocks and store-specific hours—not the entire web.

The pattern repeats: narrow, truthful context beats general intelligence for operator-facing products.

What Belongs in AI Memory?

Build a context map for every AI feature with four layers:

1. Canonical truth — pricing tables, SKU master, approved FAQ (AssetIQ inventory records, not marketing PDFs).
2. Behavioral examples — 20–50 real inputs showing how users actually phrase requests.
3. Constraints — tone, languages, escalation triggers, compliance rules (Anne's privacy-first wedge).
4. Negative space — sources explicitly excluded and why.

This pairs with the Claude stack for PMs: Projects hold research; production context holds what customers touch.

How Do You Test Context Before Launch?

Run context evals—not just output evals:

• Does retrieval return the right source for 90%+ of golden questions?
• When the answer is wrong, is it bad context or bad generation? Tag separately in your eval gates.
• After content updates, does the golden set still pass without prompt changes?

How Does Context Engineering Differ in Emerging Markets?

Connectivity and language mix force smaller, offline-friendly context packs. See emerging markets PM: cache canonical answers locally, sync when online, never assume always-on retrieval.

Explore: AI product management · case studies · frameworks