Prompting is the skill of writing clear, structured inputs to get high-quality outputs from a Large Language Model (LLM). It’s all about giving the model the right context, constraints, and guidance. Techniques like Zero-Shot, Few-Shot, and Chain-of-Thought help shape tone, reasoning, and output format.

As LLMs grow more diverse, cross-platform prompting is becoming a must-have skill.

LLM Models & Tools

Today’s AI landscape includes a range of powerful LLMs, each with its own strengths. Here’s a quick look at the major players:

• ChatGPT (OpenAI): Strong in conversation, coding, and tool integration. Supports memory, multimodal input, function calling, and browsing.

• Claude (Anthropic): Great for long-context reasoning and safe, thoughtful outputs. Ideal for complex, document-heavy tasks.

• Gemini (Google): Tightly integrated with Google tools. Handles text, image, and video, with real-time search and Workspace features.

• LLaMA (Meta): Open-source and customizable. Lightweight and ideal for private or enterprise deployment.

• Perplexity: Blends AI with live web search. Offers real-time answers, citations, and up-to-date insights.

Each model fits different needs—knowing which to use (and how to prompt it) is key.

While each LLM has its own specific parameters and requirements, the foundational approach to creating and training these models shares commonalities.

Most LLMs operate like a conversation made of different message types:

• System – Sets the tone, behavior, or rules for the model. Think of it as the “brief” or setup.

• User – The core input or question you’re asking.

• Assistant – The model’s response based on everything above.

Mastering prompt structure means knowing how to layer these messages to guide tone, reasoning, and outcomes.

Output isn’t just about the words you write—it’s also shaped by the dials you set behind the scenes. Common parameters include:

• Temperature – Controls randomness. Low = focused, High = creative.

• Top_p – Keeps output within the most likely word choices (nucleus sampling).

• Top_k – Similar to top_p, but limits to the top k possible tokens.

• Stop – Tells the model when to stop generating.

Most LLMs support some version of these. Check the docs for each platform to see what’s available—and how to tweak them for better control.

Some models support tools—like web search, image generation, or code execution.

These tools aren’t run by the model itself. Instead, the model outputs structured text (like a function call), and the external system carries out the action.

If you’re using OpenAI, the Assistant Playground is a great way to test prompts and tool integrations in real-time. You’ll need an OpenAI account to access it.

Tokens are the building blocks of text in large language models. Depending on the model, a token might be a word, subword, or even just a few characters.

Why tokens matter:

• Input & output: LLMs read and generate text as token sequences.

• Prediction: Text generated by predicting one token at a time based on statistics.

• Truncation: If a prompt exceeds the token limit, it gets cut off.

• Context limit: Models have a token limit per request.

• Pricing: Usage costs are often based on token count.

Prompt engineering is all about writing clear, structured inputs that guide AI to deliver useful, accurate results. Here’s a breakdown of core techniques and best practices to help you write better prompts—based on insights from OpenAI and leading prompt engineers.

A strong prompt sets context, defines the task, and shapes the output. Use these building blocks:

• Role + Objective: Set the tone and goal. “You are a product manager analyzing user feedback.”

• Instructions: Be direct about the task. “Summarize key pain points from the feedback below.”

• Reasoning Steps: Ask the model to think out loud. “Identify patterns, group them, then prioritize.”

• Output Format: Guide how results should look. “List top 3 issues with short explanations.”

• Context: Add any relevant background. “The product is a mobile budgeting tool for freelancers.”

• Examples: Show a clear input → output pair to anchor expectations. “Input: App is slow to load → Output: Loading speed impacts usability.”

Product Map created a simple prompt template you can adjust and reuse. Download the template to copy, paste, and customize for your own use in any LLM.

These best practices help you write clearer, more effective prompts that improve model performance and reduce errors.

• Start simple – Iterate for optimal results

• Use separators – Apply markdown titles for major sections and subsections

• Break it down – Break big tasks into simpler subtasks

• Lead with clarity – Place instructions at the start of prompts

• Use examples – Show what “good” looks like

• Be specific – Provide examples to illustrate desired formats

• Focus on what to do – Don’t ask what not to do

In-context learning lets you guide an AI model by including examples directly in the prompt. Instead of retraining, you show the model how to respond—helping it adapt to tone, structure, or format on the spot.

It’s a fast, flexible way to improve accuracy, especially when you don’t have labeled training data. The model learns from what you show—no fine-tuning required.

Foundational Prompt Types

These are simple ways to guide AI by adjusting how many examples you include in the prompt. They’re a core part of in-context learning—helping models understand and adapt to new tasks without retraining.

Zero-Shot Prompting

No examples—just an instruction. Best for simple, general tasks. Example: “Summarize this paragraph.”

One-Shot Prompting

One example sets the pattern. Useful for clear, structured tasks. Example: Show how to translate “hello,” then ask for “thank you.”

Few-Shot Prompting

2–5 examples guide tone, format, or logic. Great for nuanced or creative tasks. Example: Show 3 Q&A pairs before asking a new question.

Multi-Shot Prompting

10+ examples. Used when accuracy and pattern recognition are critical.

RAG enhances language model responses by retrieving and incorporating external information—helping the model go beyond its training cut-off and grounding outputs in current, factual data.

• Retrieval – Search for relevant data

• Augmented – Add it to the prompt context

• Generation – Use it to create grounded outputs in Gen AI

This approach improves accuracy and reduces hallucinations, especially in tasks where up-to-date or domain-specific knowledge is critical. While powerful, RAG implementation can be complex, requiring careful integration of relevant, high-quality data sources. Done well, it enables AI to generate more context-aware and trustworthy responses.

There are several techniques designed to boost model performance—especially for earlier or faster models. While newer models like ChatGPT o1 and o3 often have built-in reasoning (e.g. chain-of-thought), these methods still offer value where such capabilities aren’t native.

Chain-of-Thought Prompting

Chain-of-Thought (CoT) prompting helps AI reason through problems by breaking tasks into intermediate steps—leading to more accurate and explainable results.

• Zero-Shot CoT: Add “Let’s think step by step” to trigger structured reasoning—even with no examples.

• Few-Shot CoT: Show example problems with step-by-step solutions to guide the model through complex logic.

Self-Consistency

Generate multiple reasoning paths using few-shot CoT, then select the most consistent final answer.

Generated Knowledge Prompting

Use a structured prompt format (Input → Knowledge → Prediction) to enrich model understanding with supporting facts.

Prompt Chaining

Feed the output of one prompt into the next—ideal for multi-step workflows or layered tasks.

Tree of Thoughts (ToT)

Explore multiple reasoning paths in a tree-like structure. Useful for tasks requiring strategic thinking, lookahead, or evaluating alternative options.

Overall Trends

Prompting is becoming more natural, more flexible, and more responsible. Models now handle multimodal inputs like text, images, and documents—while simple, natural language is often enough to get strong results. At the same time, there’s growing focus on ethical prompting and responsible use.

Customer interviews & Surveys: Use LLMs to summarize transcripts and extract key themes automatically.

Solution ideation & Product experiments: Prompt AI to generate feature ideas or run simulated experiments before dev work.

Market research & Competitive analysis: Use AI search tools (like Perplexity or ChatGPT) to scan and summarize competitor moves.

Social listening & Sentiment analysis: Apply tools to monitor reviews and social mentions, highlighting top trends.

Data analysis & Insights generation: Use analytics platforms with AI assistants to uncover usage patterns and anomalies.

Vibe coding: Build working products with AI-powered tools from simple prompts.

Prompt-to-prototype workflows: Use tools like V0.dev or Loveable to go from product idea to functional UI screens.

Coding with AI copilots: Use tools like GitHub Copilot or Cursor to generate, explain, and modify code snippets faster.

LLM-powered logic simulation: Simulate feature logic using ChatGPT or Claude to validate hypotheses before development.

Automated testing: Generate test cases and QA scripts by prompting AI with feature specs or user stories.

Goal setting & OKR alignment: Use AI to generate OKRs based on product vision or previous planning docs.

Organize roadmap & Identify gaps: Use LLMs to highlight missing initiatives and suggest improvements.

Sprint planning & Dependency management: Auto-generate sprint plans and dependencies from feature descriptions.

Live replanning with AI agents: Adjust roadmaps instantly when priorities shift—AI recalculates and reassigns scope.

Role-specific task generation: Break down product work into clear assignments for frontend, backend, design, and QA.

Feedback clustering: Utilize LLMs to analyze and categorize customer feedback by theme or urgency.

Use tools like Zentrik.ai to automate your product development.

Synthetic user testing: VValidate direction through AI-led interviews with real users via Whyser by simulating feedback from synthetic personas using Synthetic Users.

Persona creation: Generate draft personas from analytics, interviews, and behavior data using LLM.

Market segmentation: Use AI to identify and label user segments from usage or CRM data.

Need & Empathy mapping: Prompt AI to synthesize emotional and functional needs from raw user inputs.

Backlog Management

Feature & Requirements writing: Use AI to turn user stories or notes into full specs and PRDs.

Scope breakdown & Feature prioritization: Generate scope outlines and apply prioritization models with AI.

Decision making & Feedback integration: Ask AI to evaluate trade-offs using feature data and strategic goals.

Theme discovery: Use AI clustering tools like Kraftful to detect recurring feature requests.

Product marketing: Draft landing pages, positioning statements, and pitch decks with AI.

Client acquisition & Sales automation: Use AI to personalize outreach emails and generate sales scripts based on CRM data.

Release notes & Announcements: Generate announcement drafts, emails, or social copy with ChatGPT or Jasper.

FAQs & Support docs: Create initial support articles from product specs using LLMs.

Notifications & UX copy: Use AI to write friendly, on-brand UX microcopy in different tones.

Post-Launch & Analytics

Behavioral & Data insights: Use AI-enhanced platforms to explain user behavior trends in plain language.

Automated reports & Experiment analysis: Summarize A/B tests or user behavior experiments with LLMs.

User feedback analysis: Use AI tools to auto-tag, cluster, and convert feedback into actionable user stories.

Product Operations

Reusable prompt libraries: Standardize prompts for idea generation, planning, or evaluations—turning them into team assets.

Organize product environments: Maintain a shared workspace with key product artifacts (e.g. feature lists, personas, design principles) to help AI generate more relevant outputs.

Cross-functional collaboration: Automate routine ops (e.g. changelogs, design-to-dev handoff, QA briefs) with custom workflows powered by Claude or GPTs.

AI can simplify your workflow from day one. OpenAI offers two powerful options depending on your needs. Both tools help PMs move faster, automate the routine, and stay focused on strategy.

Create tailored AI tools using a built-in interface—no coding required. You can customize behavior with instructions, upload docs, and share via the GPT Store.

Ideal for fast prototyping and hosted entirely within ChatGPT.

Assistants API

Build advanced AI assistants into your product or internal tools. Requires coding, but gives full control over functionality and integration.

Use Cases:

• Embedding AI in your app or platform

• Automating workflows with file handling or search

• Integrating with internal systems for deeper context

Hosted independently, with no built-in sharing—great for custom, secure deployments.

Using AI in Product Work

Start with no-code tools like Custom GPTs to support tasks such as spec writing, persona creation, or analyzing feedback. For more technical use cases, the Assistants API lets you integrate AI into internal tools or your product stack.

Focus on clear prompts, structured inputs, and small, repeatable tasks—then refine as you go.