Aligning AI responses with email thread context

ChatGPT and AI just gave me something no CRM has managed in 2 years: A clear, contextualized list of people I should be following up with, and why. Here’s what I did: I connected GPT Deep Research to my Gmail and ran a custom prompt that looked at all threads from the last 30 days. But this wasn’t just about scraping names. I gave it a real role: “Search my Gmail for any past threads with VPs, Directors, or CMOs of Marketing, Growth, or Demand Gen—especially from mid-market or enterprise SaaS, AI, or DTC brands. Prioritize companies likely spending $100K+/month on digital ads or discussing platforms like Google, Meta, and TikTokAppsFlyer. Look for threads that showed positive interest (audit, proposal, call, scaling convo) but went cold—especially if they mentioned “revisit,” “Q3,” “circle back,” or similar. Return 10 to start: name, company, role, date of last email, summary, and a quick reason to follow up now.” Basically, I turned my inbox into a memory machine. And it reminded me of: ▪️ Deals that quietly fizzled out ▪️ Warm leads that ghosted ▪️ Prospects I meant to circle back with but never did But the best part? It gave me context - not just contact names. I could immediately remember: → Who was worth re-engaging → What angle to take → What tension or timing I might’ve missed the first time Some people I followed up with instantly. Some I skipped. But all of it helped me move from reactive to intentional - in under 30 minutes. I’ve spent 10x that just trying to remember who slipped through the cracks. So no, AI’s not replacing my pipeline. But it is making me a sharper operator. Not everything in your inbox is worth revisiting. But some threads are just one smart nudge away from momentum.

Anyone can write a prompt. But only experts know how to engineer context. If you want precise, reliable, and human-like AI responses, it’s not just what you ask - it’s how much context you provide. This guide breaks down the 10 key elements that make a world-class prompt through the lens of Context Engineering: 1. Task Context – Clearly define what the model should do and in what role. 2. Tone Context – Set the voice and communication style for consistency. 3. Background Data – Add relevant documents, facts, or images for grounding. 4. Detailed Rules – Include do’s and don’ts to shape the AI’s behavior. 5. Examples – Provide sample interactions to guide response style. 6. Conversation History – Maintain continuity by giving recent context. 7. Immediate Request – Specify the current user’s question or action. 8. Step-by-Step Thinking – Encourage logical reasoning before answering. 9. Output Formatting – Tell the model how to structure its response. 10. Prefilled Response – Use starter responses to set direction or tone. When all 10 layers come together, your prompt stops being a simple query, it becomes a complete instructional environment. That’s the difference between a good answer and an expert-level interaction. What works well according to you?

I've spent an embarrassing amount of time arguing with ChatGPT 😅 "No, that's not what I meant." "Try again." "More conversational." "Okay but like... better." Turns out, the problem wasn't (100%) the AI. It was me not knowing how to actually prompt the thing 😆 We all know the type of prompts that DON'T work... "Write an email to a [prospect] about our product." You hit enter, AI spits something out, and it's... fine? Generic? Sounds like every other AI-generated email your prospects are already ignoring? After watching hundreds of marketers (and myself) struggle with this, Corrina Owens and I built a framework that actually works: Signal Context + AI Role + Job + Output Format + Tone Here's what each piece does: 🚦 Signal Context: What are we reacting to? (competitor mention, asset download, re-engaged champion) 👤 AI Role: Who is the AI acting as? (AE, CMO, data analyst) Job: What are we asking it to do? (write email, analyze patterns, generate copy) 📜 Output Format: What structure do we want? (3-paragraph email, bullet list, Slack message) 📣 Tone: What's the emotional nuance? (confident, consultative, direct) Real example from our guide: ❌ Bad prompt: "Write an email to a prospect about {{feature of our product}}" ✅ Good prompt: "You're an AE who specializes in competitive displacement. A prospect just downloaded our guide comparing us to [Competitor]. Write a 3-paragraph follow-up email that's confident and consultative, addresses the most common objections we hear about [Competitor], and ends with a specific meeting CTA to discuss their evaluation criteria." The difference? The second prompt gives AI actual context about buyer behavior, defines the role clearly, and specifies the exact output you need. This is just one of six prompting modes we break down in our AI Prompt Guide. We cover everything from multi-signal account alerts (the prompts that generate Slack-ready messages for your reps) to win-loss pattern analysis (turning 6 quarters of deal data into predictive account scoring). Copy the prompts. Adapt them to your buyers. Stop arguing with ChatGPT 😉 https://lnkd.in/gxFAEAsi

You've written the perfect prompt, but your AI agent is still failing. Why? Because the your prompt is just 5% of the input. The other 95% is the historical data, the tool definitions, the system rules is an absolute mess. We've graduated from tactical prompts. The biggest lever for AI performance today is Context Engineering and that does not mean "better RAG." It's the discipline of architecting the entire operating system for an LLM. It’s the dynamic, curated "working memory" we build for the model before it ever sees the prompt. Lazy context is why your agent loops, hallucinates, and ignores instructions. Here are 4 techniques we use internally to get our agents to perform well. 1. Your System Prompt is your AI's Constitution. Just because a prompt guru on X told you that "You are a helpful assistant. Be a lawyer" is great system prompt...its not. Define the AI's persona, rules, boundaries, and (most importantly) what it must not do. This instruction set is the most critical, persistent part of the context. Put it first. 2. Curate Memory. Sending raw unfiltered historical data won't work either. The model will suffer from the "Lost in the Middle" problem and forget key facts. Instead, engineer a memory layer: use a summarizer to create a concise "rolling summary" or a "fact sheet" of the conversation, and feed that in as context. 3. Tool Definitions Are Context. When you give an AI agents/tools via function calling, detailed tool descriptions are a critical instruction. A vague function name (e.g., "search_db") will fail. So use a precise description ("Use this function only to find a customer's order ID based on their email") is high-leverage context that controls behavior. 4. Separate and Structure All Inputs. The model needs to know what's is an "instruction," what's "interaction history," what's "retrieved data," and what's the "user query." Stop concatenating them into one messy blob. Use XML tags (<instructions>, <history_summary>, <retrieved_doc>) to create a structured information packet. If you're thinking the next 10x leap in AI will come from a 10T parameter model, it wont. It will come from organizations that master the data pipeline into the model along with architecting the entire context.

🔥 PMs: If you're building AI product, your PRD must include context requirements. Most PRDs say: “Use context to improve responses.” ❌ Too vague ✅ Not actionable 💸 Costly + leads to hallucinations + token waste Here’s what Great AI PMs actually specify 👇 1) Context Inputs: What information must the model see? Required signals (history, user prefs, files, APIs) What to ignore (noise = cost) E.g.: “Always load last x msgs + active doc. Ignore greetings + fillers.” 2) Compression Rules: What gets summarized and how aggressive? Critical vs optional info Fidelity expectations E.g.: “Preserve entities + intent. Summarize redundant chat & boilerplate.” 3) Selection Logic: What matters most, and when? E.g.: “Rank current file > open tabs > repo history > global kb.” 4) Persistence Windows: How long context lives E.g. : “User prefs forever. Project memory 90 days. Chats auto-delete in 30.” 5) Isolation: What cannot mix? E.g. : “Strict workspace isolation. No cross-project context.” 6) Orchestration: What APIs & docs to query+ acceptable latency ? E.g. : “Query internal KB + public docs. Max latency 200ms. No PII external.” Treat context like a product requirement, not an afterthought. PMs own what & why. Engineering owns how. 💬 Get full guide on Context Engineering in comments. 🔁 Repost if this helped.

Just had a major realization that's changing how I work with AI tools. We've all heard "𝐦𝐨𝐫𝐞 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 = 𝐛𝐞𝐭𝐭𝐞𝐫 𝐚𝐧𝐬𝐰𝐞𝐫𝐬" but I'm finding the opposite can be true! I've seen it firsthand - feed an LLM too much information without proper management and you get what I call "Context Distraction." The AI becomes overwhelmed, fixates on irrelevant details, and starts repeating itself instead of generating fresh insights. It's like trying to have a productive conversation with someone who's reading through a 200-page transcript of everything you've ever discussed. At some point, focus gets lost. Two approaches that have dramatically improved my results: 1️⃣ 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐒𝐮𝐦𝐦𝐚𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Instead of dumping entire conversation histories into new prompts, I summarize key points and decisions. This gives the AI a clean slate with just the essential context. 2️⃣ 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐎𝐟𝐟𝐥𝐨𝐚𝐝𝐢𝐧𝐠: Breaking complex projects into discrete conversations rather than one massive thread. I keep track externally (basic notes work fine) and only introduce relevant information when needed. The difference in output quality is remarkable. My conversations are more focused, responses are more creative, and I'm getting better solutions faster. Who else has noticed this pattern? Any other techniques you've found effective for managing AI context? #ArtificialIntelligence #LLMs #ProductivityHacks #AITools

Context Engineering vs. Prompt Engineering Most marketers are still prompt engineering. The best ones are context engineering. Here’s the difference and why it matters for your marketing and research work: PROMPT ENGINEERING is the art of asking better questions. It’s about crafting each individual request with the right structure, examples, and specificity to get quality outputs. Think: “You are an expert copywriter. Write 3 email subject lines for our Q4 campaign targeting B2B decision-makers. Keep them under 50 characters and emphasize ROI.” Useful? Absolutely. But you’ll repeat this context every. single. time. CONTEXT ENGINEERING is building the environment where better answers emerge naturally. It’s about creating a persistent knowledge system that shapes every interaction. You establish once: → Your brand voice and guidelines → Target audience psychographics and research → Competitive positioning framework→ Strategic objectives and constraints → Examples of what great looks like Now your prompts become: “Generate subject lines for the Q4 campaign.” The quality stays high. The efficiency multiplies. WHY THIS MATTERS FOR MARKETING: When you’re producing content at scale, context engineering ensures brand consistency without constant oversight. Every output, whether ad copy, email sequences, or social content automatically aligns with your brand, audience, and strategy. You’re not just faster. You’re strategically coherent across every touchpoint. WHY THIS MATTERS FOR INSIGHTS: Research is cumulative. Each study builds on previous work. Context engineering allows you to embed your research philosophy, methodological standards, and strategic questions into the system. Your analysis becomes more sophisticated over time because the AI understands how current findings connect to broader patterns. You move from “analyze this data” to “given what we learned in Q2 and Q3, what does this tell us about the trajectory?” THE EVOLUTION: Prompt engineering = asking better questions Context engineering = creating systems where all questions get answered better Most marketers are stuck at level one. The competitive advantage is in level two. Start thinking less about individual prompts and more about the architecture of knowledge and constraints you’re building. That’s where AI becomes a genuine strategic partner, not just a faster execution tool. How are you using AI in your marketing and research work? Are you still prompting, or have you made the shift to context engineering? Drop in comments below. #contextengineering #promptengineering #genai #marketing #marketresearch

Stop Getting Generic AI Output. Start Building Real Context. Most businesses are frustrated with AI because they're feeding it data without meaning. The result? Flat emails, irrelevant insights, and wasted time. The solution isn't better AI tools. It's better context. Here are the 5 layers of context every business needs, with examples: 1. Foundational Clarity: Your core business explained simply "We help B2B SaaS companies reduce churn by 40% through automated customer health scoring and intervention workflows. Our clients are typically 50-500 employees with $5M-50M ARR." 2. Customer Understanding: Document real customer language and pain points "Our customers say: 'We're losing customers and don't know why until it's too late.' They want predictable revenue and early warning signals. They use terms like 'customer health,' 'retention rate,' and 'proactive support.'" 3. Brand Tone and Voice: Teach AI how you actually sound "Write in a direct, data-driven tone. Use short sentences. Include specific metrics when possible. Avoid jargon like 'game-changing' or 'revolutionary.' Sound confident but not salesy. Think McKinsey report, not startup pitch deck." 4. Platform Consistency: Align your story everywhere "Our LinkedIn company page, website about section, and sales deck should all emphasize the same three benefits: reduced churn, increased revenue predictability, and faster customer issue resolution." 5. Process Transparency: Map your actual workflows "When a lead books a demo, they get an email within 2 hours with a prep guide. The demo focuses on their specific use case. Follow-up happens within 24 hours with a custom proposal. Include these steps in any sales sequence you create." Example: [Before] "Write a follow-up email to a new lead." [After] "Write a follow-up email to Sarah, a VP of Customer Success at a 200-person SaaS company. She mentioned on our demo that they're losing 15% of customers annually and have no early warning system. Use a consultative tone. Reference our case study with TechCorp (similar size, reduced churn from 18% to 7%). Include a specific next step." The difference? The second email sounds human because the AI understands the human context. AI agents are becoming the default way customers discover businesses. If your business lacks clear context, you won't even be recommended. But if you build proper context? The machine becomes your best salesperson. What's your biggest AI context challenge? Share in the comments.

You're ruining your AI outputs. And you don't even know it. It's called "context clashing." Here's what happens: You ask AI to write an email. AI asks what kind of email. You say "professional email." AI asks to whom. You say "to Bob." AI asks about the topic. Back and forth. Back and forth. Each exchange degrades the output quality. Your AI gets dumber with every question. Instead, front-load everything: "Write a professional email to Bob Johnson, our project manager, about the Q4 budget review meeting scheduled for next Tuesday. Use a formal but friendly tone. Include the agenda items we discussed and request his input on the marketing spend allocation." One prompt. All context. Better results. Stop the ping-pong. Start with precision. --- P.S. If you want my free 30-day AI insights series, comment "Purple Unicorn" below. 🦄

𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐢𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐞𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐥𝐲 𝐟𝐨𝐫 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬. If you want your AI agents to reason, plan, and act like intelligent collaborators instead of fancy autocomplete tools, you need to master one concept: context. 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐩𝐨𝐰𝐞𝐫𝐬 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐲 𝐢𝐧 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 (𝐚𝐧𝐝 𝐡𝐨𝐰 𝐲𝐨𝐮 𝐜𝐚𝐧 𝐮𝐬𝐞 𝐢𝐭 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐥𝐲): 𝟏. 𝐈𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: 𝐖𝐡𝐚𝐭 𝐲𝐨𝐮 𝐭𝐞𝐥𝐥 𝐭𝐡𝐞 𝐚𝐠𝐞𝐧𝐭 This is where you define the mission. * Role (Who): Give the agent an identity PM, coding agent, researcher. * Objective (Why): Explain the bigger picture, not just the task. * Requirements (How): Specify reasoning steps, style, constraints, and response format. * Why it matters: Without clear instructions, agents drift into vague or irrelevant outputs. 𝟐. 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐚𝐠𝐞𝐧𝐭 𝐤𝐧𝐨𝐰𝐬 Agents perform better when they understand the environment and data around them. * External Context: Business strategy, workflows, domain facts. * Task Context: Documents, structured data, product specs, tickets. * Why it matters: It grounds the agent in the real world connecting reasoning with reality. 𝟑. 𝐌𝐞𝐦𝐨𝐫𝐲 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐚𝐠𝐞𝐧𝐭 𝐫𝐞𝐦𝐞𝐦𝐛𝐞𝐫𝐬 Real intelligence requires memory not just instant recall. * Short-term: Chat history, progress state, reasoning steps within a session. * Long-term: Semantic (facts), episodic (past interactions), procedural (previous instructions). * Why it matters: Memory turns reactive tools into proactive teammates agents that *learn, adapt, and improve* over time. When these three layers work together Instructions, Knowledge, and Memory AI agents move beyond just generating text. They start reasoning, acting, and collaborating just like human team members ♻️ Repost this to help your network design better AI agents ➕ Follow Sathish Kumar Subramani for more deep dives on AI agents, context, and real-world use cases #AIAgents #AgenticAI #LLM #GenAI