As product managers, we’re always hunting for ways to infuse AI into our roadmaps in ways that are both powerful and pragmatic. OpenAI’s GPT-4.1 Prompting Guide lays out concrete tactics for squeezing every ounce of capability out of the new model family. Below, I’ll distill its core lessons, critique where it stumbles from a product lens, and offer simple next steps for less-experienced PMs keen to experiment with AI today.

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What’s in the Guide: Key Takeaways

1. GPT-4.1 Is Ultra-SteerableUnlike prior versions that “guessed” intent, GPT-4.1 follows instructions literally—and rewards you for being crystal clear. A single clarifying sentence can correct its course mid-response OpenAI Cookbook.
2. Agentic Workflows FTWBuild “agents” that autonomously tackle multi-step problems by including three reminders in your system prompt:
3. Persistence: “Keep going until the query is completely resolved.”
4. Tool-calling: “Use your tools; do not guess.”
5. Planning (optional): “Plan and reflect before each action.”These tips boosted OpenAI’s internal coding benchmark performance by nearly 20% OpenAI Cookbook.
6. Use the Tools API, Not Manual HacksPass your tool definitions directly via the API’s tools field rather than hard-coding schemas into prompts. This simple switch yielded a 2% gain in code-fix accuracy in OpenAI’s tests OpenAI Cookbook.
7. Induce “Chain-of-Thought” with PromptsGPT-4.1 isn’t inherently a reasoning model, but you can make it think out loud by explicitly asking for step-by-step plans. This raised pass rates by ~4% on complex tasks OpenAI Cookbook.
8. A Real-World Agentic ExampleThe guide even shares the exact system prompt used to fix open-source bugs end-to-end—complete with rigorous testing, reflections on edge-cases, and instructions to “never end your turn” until success OpenAI Cookbook.

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What I Love 

• Data-Backed Tips: Every recommendation is tied to measurable gains (e.g., +20% SWE-bench). This aligns with our obsession over OKRs and evidence-based decisions.
• Actionable Examples: The agentic sample prompt is copy-and-paste ready, making it trivial to kick off your first experiment.
• Focus on Maintainability: Emphasizing the API’s tools field discourages brittle, manual parsers—just like we prefer scalable, well-documented APIs in our own products.

What I’d Add or Tweak 

• Broader Use Cases: The guide centers heavily on coding workflows. As PMs building customer-facing features, we need parallel sections on summarization, classification, or conversational UI best practices.
• Evaluation Framework: While they stress “build informative evals,” there’s no template for A/B testing prompts or tracking KPIs like completion quality or hallucination rate.
• Product-Facing Pitfalls: The guide doesn’t warn against over-engineering prompts or neglecting user feedback loops—common traps when PMs first dive into AI.