Context Engineering for Product Managers: Building Successful AI Products
At this year’s AI Product Summit, product leaders shared a new kind of toolkit for leveraging AI. Vikash Rungta, Co-Founder at Alloi.ai and former product manager for Meta’s Llama team, took the stage to spotlight a trend that’s redefining what it means to build great AI products:
“Context engineering.”
The conversation around this new term has gained momentum fast. Back in July, Tobi Lütke (CEO of Shopify) described it as the real skill behind making large language models (LLMs) reliable and useful.
Since then, other experts like Tesla’s Andrej Karpathy have echoed that the future of AI is shaped less by clever prompts and more by the way teams curate and deliver information to their models.
“There is a big misconception that LLMs can actually have a memory they learn over time. LLMs don't have memory; they're stateless. So every time you ask a question, you have to give the entire context to the LLM.” — Vikash
Context engineering is the next evolution in AI product development. It’s about designing the environment and inputs that let your models perform at their best, every time a user interacts. This shift unlocks new levels of reliability and personalization for AI-powered experiences.
What Product Managers Need to Know About Context Engineering
For product managers (PMs), context is the foundation of every decision, every roadmap shift, and every customer conversation. With AI-powered tools now woven into each stage of the product lifecycle, the way you manage context shapes your team’s ability to deliver outcomes.
Context engineering relies on memory. Vikash outlined a three-layer memory system:
- Short-term memory: Captures recent interactions and follow-up questions within a chat or workflow, so the AI doesn’t lose the thread of the current task.
- Mid-term memory: Summarizes patterns and key goals that emerge over a session, distilling signals like “this user wants to book a family trip to Italy for a week.”
- Long-term memory: Encodes enduring user preferences, such as travel style or dietary needs, becoming the real differentiator over time.
When you combine all three, your AI can keep track of session context while personalizing responses with the full history of each user or team.
The payoff is real. The advantage of context engineering over prompt engineering is: increasingly accurate insights, fewer misunderstandings, and smarter automation.
Vikash Rungta’s example of how context engineering improves AI outputs.
Vikash also stressed the importance of intentional design, which requires isolation. Context engineering for product managers means actively curating what information your AI systems see and use. Instead of dumping everything into an LLM, successful teams select (or isolate) the most relevant customer insights, requirements, and history for each product conversation or workflow.
“If you can isolate the relevant context, your agents are going to start to become important. A trip planner agent that knows that the person loves art, hates crowds, and is vegan can generate a much personalized, much more specific itinerary for the person and the need of the hour.” — Vikash
Teams that try to solve context problems by flooding AI with every piece of available data quickly hit roadblocks, such as higher costs or less accurate results. As more data enters the system, it gets harder for both models and humans to focus on what actually matters. Anthropic refers to this as “context rot,” and it’s exactly why isolation is critical.
How and When Product Managers Can Use Context Engineering Successfully
Your job as a product manager is to ensure the right context is always available—at the right time, and in the right place. Product managers should focus on curating the smallest possible set of high-signal information that enables great product decisions.
Product managers can successfully do this by treating context as a product. Iterate, prune, and measure what you keep by:
- Cleaning up noisy data. Review which tools are duplicating information or creating silos.
- Building regular habits around summarizing customer insights and updating what’s most important for your team.
- Reviewing and streamlining your integrations so that only valuable, up-to-date information gets pulled into your AI workflows.
- Avoiding silos. Design your stack so AI can retrieve just the right data at just the right time.
So, when should PMs apply context engineering? Anytime you are designing workflows that rely on AI to deliver insights, automate routine tasks, or personalize experiences—especially when those workflows require pulling in feedback, historical data, or multi-step reasoning.
This approach is most valuable during product discovery, prioritization, customer feedback analysis, and roadmap planning, but it can make a difference at every stage where decision quality depends on the information available to your tools and teams.
Where We’re at Now
Every great AI product starts with context, engineered deliberately.
Context engineering is still a nascent discipline, but it’s quickly becoming core to how product teams build at scale. Early approaches relied on clever prompts and manual workflows. Now, the field is shifting. Teams are moving from intuition and guesswork to intentional, data-driven systems for curating, updating, and delivering just the right information at every step. The difference is already visible in AI-powered products that personalize at scale and adapt in real time to changing needs.
Productboard Spark is an example of what’s possible when you combine thoughtful context engineering with intelligent product management. Spark tackles context fragmentation and knowledge loss by anchoring PRDs, customer research synthesis, and competitive analysis in real product context. Every insight is rooted in your customers, your market, and your value-driven strategy.
For many organizations, context engineering is still evolving—few have the robust memory systems in place for context-aware workflows. But the pace of change is accelerating. Each new advancement in model architecture and platform extensibility makes it easier to move beyond “prompt engineering” and toward dynamic, high-signal context for every decision.
Curious how top PMs transform brittle prompts into reliable, defensible AI products? Vikash’s session is packed with practical frameworks and real-world scenarios to help you build AI features that scale, retain users, and drive measurable impact.
