How to Use Prompt Chaining for Better AI Results

🚀 𝗛𝗼𝘄 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 AI Agents are becoming the backbone of next-generation applications, from intelligent assistants to autonomous decision-makers. But with so many concepts to grasp, where should you start? Here’s a 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗽𝗮𝘁𝗵 to help you go from beginner to building powerful AI agents: 🔹 𝗟𝗲𝘃𝗲𝗹 𝟭: 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 – 𝗚𝗲𝗻𝗔𝗜 & 𝗥𝗔𝗚 1 Understand 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗯𝗮𝘀𝗶𝗰𝘀 – what it is, where it’s used, and its limitations. 2 Learn the 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗼𝗳 𝗟𝗟𝗠𝘀 (how they work, fine-tuning, embeddings). 3 Master 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 to communicate effectively with models. 4 Dive into 𝗗𝗮𝘁𝗮 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 – because good AI is built on clean, well-structured data. 5 Explore 𝗔𝗣𝗜 𝗪𝗿𝗮𝗽𝗽𝗲𝗿𝘀 & 𝗥𝗔𝗚 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀 – critical for connecting external knowledge sources. 🔹 𝗟𝗲𝘃𝗲𝗹 𝟮: 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 1 Get an 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 – their purpose, structure, and real-world applications. 2 Learn 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 (LangChain, LlamaIndex, Haystack, etc.). 3 Practice by 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗦𝗶𝗺𝗽𝗹𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 – start small, then iterate. 4 Understand the 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 – planning, reasoning, execution. 5 Explore 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 – giving agents context and feedback loops. 6 Move into 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 – scaling intelligence through cooperation. ✅ By following this path, you’ll not only gain theoretical knowledge but also build the practical skills needed to design scalable, reliable, and context-aware AI agents. 🌍 The AI ecosystem is evolving rapidly, those who master agents will define the next wave of innovation. 👉 What stage are you currently at in your AI Agent learning journey? #GenAI #AgenticAI #AIAgents #LLM

🚀 Just built my first RAG system!! Here’s what it does: ✔ Classifies questions and decides if the query is relevant ✔ Retrieves knowledge from documents (like manuals) ✔ Generates clear responses using an LLM ✔ Declines gracefully if the query is out of scope. This hands-on project gave me exposure to building an "AI-powered support workflow" where the system doesn’t just generate answers blindly, but actually pulls facts from real data sources. That’s what makes RAG so powerful in real-world applications. 💡 Key takeaway: Even with a simple flow, RAG can create intelligent, trustworthy assistants — something I see becoming foundational in customer support, knowledge management, and beyond. This is an early step in my AI journey, but it felt great to see it working end-to-end. Next up → experimenting with larger knowledge bases and more complex use cases. 👉 Curious to hear from others here: How are you experimenting with RAG systems or knowledge-aware AI bots? #AI #MachineLearning #RAG #GenerativeAI #LearningInPublic

🚀 **Day 6 of My Agentic AI Learning Journey** 🚀 Every new day in this journey brings a deeper understanding of how **AI agents can truly think, decide, and act independently**. On **Day 6**, I explored some of the most crucial concepts that shape the intelligence of these agents: 🔹 **Memory & Context Handling** – How agents remember past interactions and use them for smarter future decisions. 🔹 **Long-Term vs. Short-Term Memory** – Structuring memory storage for efficiency and scalability. 🔹 **Knowledge Integration** – Connecting agents with external knowledge bases, APIs, and data sources. 🔹 **Retrieval-Augmented Generation (RAG)** – Equipping agents to fetch real-time, relevant information before responding. 🔹 **Personalization** – Building agents that adapt to users' preferences, behaviors, and goals. ✨ **Key Takeaway:** Day 6 emphasized that **memory is the backbone of intelligence**. Without it, agents would just be reactive. With it, they evolve into adaptive systems that **learn, grow, and serve more meaningfully**. 🌟 **Why It Matters for the Future of Work:** Imagine customer service bots that **remember user history**, financial advisors that **learn investor patterns**, or learning platforms that **personalize education in real-time**. This is where Agentic AI is headed — and we’re just at the beginning. I’m thrilled to continue this journey and build **practical, industry-ready AI solutions**. #AgenticAI #ArtificialIntelligence #MachineLearning #GenerativeAI #FutureOfWork #AIInnovation #LearningJourney

We Don't Just Talk AI, We Use It: Our Go-To AI Toolkit   At our core, we believe in practical application. Theory is one thing, but real results come from walking the walk. Here are the top AI tools that make our team's lives easier and help us deliver for our clients.   Gemini: We've found Gemini excels at generating "no-nonsense" text, making it great for refining proposals and drafting clear communications. Perplexity: When we need deep, sourced information, we turn to Perplexity. It’s a powerful research assistant that goes beyond simple answers by providing direct sources. Gamma: It allows us to build professional offers and slide decks in minutes, not hours. The AI handles the basic structure and design, so we can focus on refining the core message. Lovable: For quick prototyping, Lovable is a fantastic tool. It allows us to test new ideas without getting bogged down in extensive development, helping us to validate concepts efficiently. Clay: We use Clay for outreach and personalisation automation. It automates the data-gathering for our outreach campaigns, allowing us to send personalised communications at scale. Custom Bot: For internal needs, we have a custom-made bot that uses our past project data. This helps us with proposal writing by providing a foundation of previous experience, ensuring consistency and quality.   The right tools, applied correctly, make all the difference. #aitools #ai #aitoolkit

🚀 Just created "The #Claude #Sonnet 4 #Prompting Playbook" - your guide to getting 10x better results from AI After experimenting with #Claude Sonnet 4, I've distilled the most effective #prompting strategies into one comprehensive guide. Why this matters: → The right tone can #transform generic responses into expert-level insights → Proper #frameworks turn vague requests into actionable outcomes. → Strategic #prompting saves hours of back-and-forth refinement The discoveries: ✅ Role-based prompting ("Act as a product manager...") produces more targeted results ✅ Step-by-step decomposition dramatically improves #reliability ✅ Multi-tool collaboration unlocks complex problem-solving capabilities ✅ Constraint-based prompting increases precision and usefulness Game-changing prompt examples included: 🔸"Research three topics and collaborate to create a comprehensive blog post." 🔸"Break down [complex topic] using analogies for different expertise levels" The difference between a basic #prompt and a #strategic one is often the difference between generic output and genuinely valuable #insights. #AI #Claude #Productivity #PromptEngineering #DigitalTransformation #ArtificialIntelligence #UntanglingAI

AI doesn’t just need data. It needs feedback. Human in the loop is the best way! Even the best RAG pipelines miss the mark sometimes: vague insights, over-generalizations, or context that doesn’t align with what humans find useful. That’s where human-in-the-loop feedback comes in. Instead of treating the pipeline as “finished,” feedback transforms it into a living system that learns from mistakes. Example: Headphone review analysis Output insight from pipeline: “Battery is bad.” Product managers tagged it as Not actionable (too vague). Preferred insight: “62% of users report battery life under 3 hours, limiting portability.” Feedback Integration feedback_log = [ {"insight": "Battery is bad", "label": "not_actionable"}, {"insight": "Battery drains <3h in 62% of reviews", "label": "actionable"} ] # Update retrieval/re-ranking to prefer quantifiable insights def adjust_weights(candidate, feedback_log): if "not_actionable" in [f['label'] for f in feedback_log if f['insight'] == candidate]: return -1 # penalize vague insights return +1 # Apply during orchestration scored_candidates = [(c, adjust_weights(c, feedback_log)) for c in candidates] Role in Orchestration 1. Retrieval → Extract relevant reviews. 2. Re-ranking → Human feedback improves scoring (boosts detailed, penalizes vague). 3. Summarization → Guided by what users mark as “actionable.” 4. Metadata → Feedback helps tune what “relevance” means in practice. 5. LLM Output → Produces insights humans actually trust and use. Why it matters Feedback closes the loop between AI outputs and business needs. Turns generic extraction into business-aligned intelligence. In a specific project: adding feedback loops doubled the rate of “actionable insights” accepted by product teams. Without feedback, the pipeline is static. With it, you’re engineering an evolving system. How are you embedding human feedback into your AI pipelines — as an afterthought, or as part of the orchestration itself? #AI #RAG #ContextEngineering #HumanInTheLoop #AppliedAI #GenerativeAI #CXO #Leadership #Systemsengineering

Helping people take their very first step with AI? 🌱 This short guide highlights 𝗳𝗶𝘃𝗲 𝗽𝗼𝗽𝘂𝗹𝗮𝗿 𝗔𝗜 𝗨𝗫 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 that make AI products easier to start with. From tackling blank-page paralysis with starter prompts to gradually introducing context and controls, these patterns are small nudges that reduce friction and build confidence.  1. 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 - Guide users through structured questions instead of leaving them to guess what AI needs.  2. 𝗚𝘂𝗶𝗱𝗲𝗱 𝗽𝗿𝗼𝗺𝗽𝘁 𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 – Provide fill-in-the-blank style builders to help users craft effective prompts with less effort.  3. 𝗦𝘁𝗮𝗿𝘁𝗲𝗿 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 – Offer pre-written examples to overcome blank-page paralysis and show users how to begin.  4. 𝗦𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀 (𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀) – Let users show examples or upload references when they can’t articulate requests in words.  5. 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗱𝗶𝘀𝗰𝗹𝗼𝘀𝘂𝗿𝗲 – Introduce advanced controls and options gradually, reducing overwhelm while building user understanding If you’re exploring AI experiences, I’d be glad to hear how you’ve approached onboarding. 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 - Shape of AI - Emily Campbell - Exploring the Innovation Opportunities for Pre-trained Models, Minjung Park - Google PAIR #GenerativeAI #productmanagement #ProductDesign #uiux #DesignThinking #ArtificialIntelligence

𝐒𝐭𝐨𝐩 𝐚𝐬𝐤𝐢𝐧𝐠 𝐀𝐈 𝐟𝐨𝐫 𝐩𝐞𝐫𝐟𝐞𝐜𝐭 𝐨𝐮𝐭𝐩𝐮𝐭 𝐢𝐧 𝐨𝐧𝐞 𝐬𝐡𝐨𝐭. Try this instead: Plan → Execute → Check → Refine Recent AI research shows smaller models can outperform massive ones when they use iterative refinement. You can apply the same principle today with any AI tool. 𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬 Instead of "write perfect email subject lines," try: Plan: List 3 criteria for good subject lines Execute: Generate 5 options Check: Do they meet your criteria? Refine: If not, improve the weakest ones 𝐖𝐡𝐞𝐫𝐞 𝐢𝐭 𝐡𝐞𝐥𝐩𝐬 Marketing: Tighter briefs, fewer revisions Sales: Better prospect research, cleaner outreach Operations: Clearer processes, fewer back-and-forth loops Data work: Catch errors before they compound Content: Structure first, polish second 𝐓𝐫𝐲 𝐢𝐭 𝐭𝐡𝐢𝐬 𝐰𝐞𝐞𝐤 Pick one repetitive task. Before you start: 1️⃣ Define "good enough" - What are 3 non-negotiable criteria? 2️⃣ Work in steps - Ask AI to do one piece at a time 3️⃣ Check your work - Does it meet your criteria? If not, ask for specific improvements 𝐒𝐢𝐦𝐩𝐥𝐞 𝐩𝐫𝐨𝐦𝐩𝐭 𝐭𝐞𝐦𝐩𝐥𝐚𝐭𝐞 "First, help me define 3 success criteria for [task]. Then do step 1 only. I'll tell you if it meets the criteria before we continue." What's one task where you'd benefit from AI checking its own work before showing you the final result? #AI #Productivity #WorkflowDesign #ProcessImprovement

If your AI outputs feel random, it’s because you didn’t define the level or the win metric. That’s where the FLOW framework comes in. It keeps your prompts focused on outcomes, not just words. F = Function → what you want AI to do L = Level → depth or complexity you expect O = Output → the format you want W = Win Metric → what success looks like Example: Flat: “Summarize this report.” FLOW: Function: “Summarize the report” Level: “For a senior executive who only has 1 minute” Output: “3 bullet points” Win Metric: “Each bullet should highlight a decision or risk” The difference? The first version gives you fluff. The second gives you actionable insights. When to use FLOW: Perfect for executive summaries, dashboards, briefs, or decision-focused outputs. Meta-Prompt to Try: “Function = [task]. Level = [depth]. Output = [format]. Win Metric = [what success looks like].” This is Day 11 of my 30-day Prompting Tips & Tricks series. Follow me to catch Day 12 tomorrow — where we’ll dive into contrarian prompting. #Ai #prompts #llm #product #Pm #agents #llm #chatgpt

Everyone’s talking about Generative AI—but what does it actually feel like to learn the tools, build POCs, and test them yourself? Here’s my journey so far… A few weeks ago, I set out to understand the buzz around Generative AI, its true purpose excluding the fact that it's only for producing images for marketing purposes and fun. What started as curiosity quickly turned into hands-on exploration—experimenting with tools and frameworks that are shaping this space. I’ve been learning the basics of: ✨ LangChain & LangGraph – to structure agent workflows ✨ RAG (Retrieval-Augmented Generation) & CAG (Context-Augmented Generation) – to ground results and cut down hallucinations ✨ APIs from Groq, Hugging Face, OpenAI, Ollama – to test models and deployment options ✨ Gen AI, AI Agents and Agentic AI ✨ Anthropic's MCP (Model Context Protocol) – and even trying its integration with Claude Desktop to extend how LLMs interact with tools and data. Along the way, I realized something important: Building value-driven AI solutions isn’t just about powerful models—it’s about grounding outputs in trusted data and keeping a Human-in-the-Loop (HITL) to ensure accuracy and responsibility. I’m still early in the journey, but each POC I build gives me more confidence in how these frameworks can come together to solve real-world problems. 👉 My intention is simple: gain a fair understanding of these tools today, to scale it responsibly, value-driven solutions in the future. If you’re also learning or experimenting in this space, I’d love to connect. The journey is always better when shared. #GenerativeAI #POC #LearningJourney #artificialintelligence #projectmanagement