Strategies For Building Conversational AI With NLP

I've tested over 20 AI agent frameworks in the past 2 years. Building with them, breaking them, trying to make them work in real scenarios. Here's the brutal truth: 99% of them fail when real customers show up. Most are impressive in demos but struggle with actual conversations. Then I came across Parlant in the conversational AI space. And it's genuinely different. Here's what caught my attention: 1. The Engineering behind it: 40,000 lines of optimized code backed by 30,000 lines of tests. That tells you how much real-world complexity they've actually solved. 2. It works out of the box: You get a managed conversational agent in about 3 minutes that handles conversations better than most frameworks I've tried. 3. Conversation Modeling Approach: Instead of rigid flowcharts or unreliable system prompts, they use something called "Conversation Modeling." Here's how it actually works: 1. Contextual Guidelines: ↳ Every behavior is defined as a specific guideline. ↳ Condition: "Customer wants to return an item" ↳ Action: "Get order number and item name, then help them return it" 2. Controlled Tool Usage: ↳ Tools are tied to specific guidelines ↳ No random LLM decisions about when to call APIs ↳ Your tools only run when the guideline conditions are met. 3. Utterances Feature: ↳ Checks for pre-approved response templates first ↳ Uses those templates when available ↳ Automatically fills in dynamic data (like flight info or account numbers) ↳ Only falls back to generation when no template exists What I Really Like: It scales with your needs. You can add more behavioral nuance as you grow without breaking existing functionality. What's even better? It works with ALL major LLM providers - OpenAI, Gemini, Llama 3, Anthropic, and more. For anyone building conversational AI, especially in regulated industries, this approach makes sense. Your agents can now be both conversational AND compliant. AI Agent that actually does what you tell it to do. If you’re serious about building customer support agents and tired of flaky behavior, try Parlant.

Human conversation is interactive. As others speak you are thinking about what they are saying and identifying the best thread to continue the dialogue. Current LLMs wait for their interlocutor. Getting AI to think during interaction instead of only when prompted can generate more intuitive and engaging Humans + AI interaction and collaboration. Here are some of the key ideas in the paper "Interacting with Thoughtful AI" from a team at UCLA, including some interesting prototypes. 🧠 AI that continuously thinks enhances interaction. Unlike traditional AI, which waits for user input before responding, Thoughtful AI autonomously generates, refines, and shares its thought process during interactions. This enables real-time cognitive alignment, making AI feel more proactive and collaborative rather than just reactive. 🔄 Moving from turn-based to full-duplex AI. Traditional AI follows a rigid turn-taking model: users ask a question, AI responds, then it idles. Thoughtful AI introduces a full-duplex process where AI continuously thinks alongside the user, anticipating needs and evolving its responses dynamically. This shift allows AI to be more adaptive and context-aware. 🚀 AI can initiate actions, not just react. Instead of waiting for prompts, Thoughtful AI has an intrinsic drive to take initiative. It can anticipate user needs, generate ideas independently, and contribute proactively—similar to a human brainstorming partner. This makes AI more useful in tasks requiring ongoing creativity and planning. 🎨 A shared cognitive space between AI and users. Rather than isolated question-answer cycles, Thoughtful AI fosters a collaborative environment where AI and users iteratively build on each other’s ideas. This can manifest as interactive thought previews, real-time updates, or AI-generated annotations in digital workspaces. 💬 Example: Conversational AI with "inner thoughts." A prototype called Inner Thoughts lets AI internally generate and evaluate potential contributions before speaking. Instead of blindly responding, it decides when to engage based on conversational relevance, making AI interactions feel more natural and meaningful. 📝 Example: Interactive AI-generated thoughts. Another project, Interactive Thoughts, allows users to see and refine AI’s reasoning in real-time before a final response is given. This approach reduces miscommunication, enhances trust, and makes AI outputs more useful by aligning them with user intent earlier in the process. 🔮 A shift in human-AI collaboration. If AI continuously thinks and shares thoughts, it may reshape how humans approach problem-solving, creativity, and decision-making. Thoughtful AI could become a cognitive partner, rather than just an information provider, changing the way people work and interact with machines. More from the edge of Humans + AI collaboration and potential coming.

I’ve been experimenting with ways to bring AI into the everyday work of telco — not as an abstract idea, but as something our teams and customers can use. On a recent build, I created a live chat agent I put together in about 30 minutes using n8n, the open-source workflow automation tool. No code, no complex dev cycle — just practical integration. The result is an agent that handles real-time queries, pulls live data, and remembers context across conversations. We’ve already embedded it into our support ecosystem, and it’s cut tickets by almost 30% in early trials. Here’s how I approached it: Step 1: Environment I used n8n Cloud for simplicity (self-hosting via Docker or npm is also an option). Make sure you have API keys handy for a chat model — OpenAI’s GPT-4o-mini, Google Gemini, or even Grok if you want xAI flair. Step 2: Workflow In n8n, I created a new workflow. Think of it as a flowchart — each “node” is a building block. Step 3: Chat Trigger Added the Chat Trigger node to listen for incoming messages. At first, I kept it local for testing, but you can later expose it via webhook to deploy publicly. Step 4: AI Agent Connected the trigger to an AI Agent node. Here you can customise prompts — for example: “You are a helpful support agent for ViewQwest, specialising in broadband queries – always reply professionally and empathetically.” Step 5: Model Integration Attached a Chat Model node, plugged in API credentials, and tuned settings like temperature and max tokens. This is where the “human-like” responses start to come alive. Step 6: Memory Added a Window Buffer Memory node to keep track of context across 5–10 messages. Enough to remember a customer’s earlier question about plan upgrades, without driving up costs. Step 7: Tools Integrated extras like SerpAPI for live web searches, a calculator for bill estimates, and even CRM access (e.g., Postgres). The AI Agent decides when to use them depending on the query. Step 8: Deploy Tested with the built-in chat window (“What’s the best fiber plan for gaming?”). Debugged in the logs, then activated and shared the public URL. From there, embedding in a website, Slack, or WhatsApp is just another node away. The result is a responsive, contextual AI chat agent that scales effortlessly — and it didn’t take a dev team to get there. Tools like n8n are lowering the barrier to AI adoption, making it accessible for anyone willing to experiment. If you’re building in this space—what’s your go-to AI tool right now?

Secret sauce for using AI and ChatGPT effectively! 🌐 Define the Chatbot's Identity: Don't just interact, assign a role! Direct ChatGPT like a seasoned director guiding an actor. For instance, when you need a 'Statistical Sleuth' to dive into data or a 'Grammar Guru' for language learning, this focused identity sharpens the conversation. Example: Instead of "Do something with this data," say "As a statistical analyst, identify and explain key trends in this data set." 🎯 Provide Crystal-Clear Prompts: Be the maestro of your requests. Precise prompts equal precise AI responses. From dissecting datasets to spinning stories, the detail you provide is the detail you'll receive. Example: Swap "Write something on AI ethics" with "Compose a detailed article on AI ethics, emphasizing transparency, accountability, and privacy." 🧠 Break It Down: Approach complex problems like a master chef—layer by layer. Guide ChatGPT through your query's intricacies for a gourmet dish of nuanced answers. Example: Replace "Help me with my project" with "Outline the process for creating a machine learning model for predicting real estate prices, starting with data collection." 📈 Iterate and Optimize: Don't settle. Use ChatGPT's responses as raw material, and refine your inquiries to sculpt your masterpiece of understanding. Example: Transform "Your last response wasn't helpful" into "Elaborate on how overfitting can be identified and mitigated in model training." 🚀 Implement and Innovate: Take the AI-generated knowledge and weave it into your projects. Always be on the lookout for novel ways to integrate AI's prowess into your work. Example: Change "I read your insights" to "Apply the insights on predictive analytics into creating a dynamic recommendation engine for retail platforms." By incorporating these strategies, you're not just querying AI—you're conversing with a dynamic partner in innovation. Get ready to lead the curve with AI as your collaborative ally in the realms of #TechInnovation, #FutureOfWork, #AI, #MachineLearning, #DataScience, and #ChatGPT! Is there anything else you would add to this secret sauce?

The people saving 10+ hours a week with AI and the people who quit after a week are using the exact same tools. A big difference, is how they prompt: Most people try AI for a week, get outputs that sound like a corporate intern wrote them, and quit. "AI just doesn't work for me." But here's what's actually happening. They type "write me an email" or "help me with this doc" and then get frustrated when Claude gives them something generic. Of course it's generic… you gave it nothing to work with. That's like walking into a restaurant and saying "bring me food" and being annoyed when you don't get exactly what you wanted. Here's a framework to write prompts that makes Claude actually useful (in under a minute) 1/ Who "You are a [expertise level] [role] with deep knowledge of [domain/industry]." Without this, Claude defaults to a generic assistant. 2/ What "Your task is to [specific deliverable] for [who it's for]." The clearer the task, the less cycles. 3/ Context Who is your audience: [role, seniority, pain points] What's the situation: [relevant background, what's been tried, what matters] Files to reference: [upload them] This is what separates your answer from everyone else's. 4/ Format → Length: [word count / number of slides / bullets] → Structure: [headers / numbered list] → Tone: [conversational / formal / punchy] → Delivered as: [plain text / markdown / copy-paste ready] This stops Claude from defaulting to generic best practices. 5/ Constraints → Avoid: [words, phrases, styles to stay away from] → Don't: [common tendencies you want to cut] → Never: [hard limits] Without constraints, Claude writes the same for everyone. 6/ Examples "Here are 3–5 examples of how the output should look: [paste them]" "Here is what I don't want: [paste 2–3 bad examples]. Here's why: [explain]" 1 good example can help more than 3 paragraphs of instructions! 7/ Success criteria → Emotion: what should the reader feel? → Intent: what should they do after? → Core problem: what are you really solving? → Detail level: high-level overview vs. deep tactical breakdown → Format check: does it match what you asked for in section 4? This is how you QA the output, without doing it yourself. 8/ Before you begin → Ask clarifying questions if anything is unclear → Don't infer or assume anything → Restate the task in one sentence and confirm before starting This sets the tone before Claude writes a single word. P.S. you can also put this in memory too so you don’t have to repeat yourself. 📌 Want a high-res PDF of this sheet? Get it here: https://lnkd.in/gKzZUq-b ♻️ Repost to help your network get more out of Claude. ➕ Follow me (Will McTighe) for more like this.

I used this guide to build 10+ AI Agents Here're my 10 actionable items: 1. Turn your agent into a note-taking machine → Dump plans, decisions, and results into state objects outside the context window → Use scratchpad files or runtime state that persists during sessions → Stop cramming everything into messages - treat state like external storage 2. Be ridiculously picky about what gets into context → Use embeddings to grab only memories that matter for current tasks → Keep simple rules files (like CLAUDE md) that always load → Filter tool descriptions with RAG so agents aren't confused by irrelevant tools 3. Build a memory system that remembers useful stuff → Create semantic, episodic, and procedural memory buckets for facts, experiences, instructions → Use knowledge graphs when embeddings fail for relationship-based retrieval → Avoid ChatGPT's mistake of pulling random location data into unrelated requests 4. Compress like your context window costs $1000 per token → Set auto-summarization at 95% context capacity with no exceptions → Trim old messages with simple heuristics: keep recent, dump middle → Post-process heavy tool outputs immediately - search results don't live forever 5. Split your agent into specialized mini-agents → Give each sub-agent one job and its own isolated context window → Hand off context with quick summaries, not full message histories → Run sub-agents in parallel when possible for isolated exploration 6. Sandbox the heavy stuff away from your LLM → Execute code in environments that isolate objects from context → Store images, files, complex data outside the context window → Only pull summary info back - full objects stay in sandbox 7. Make summarization smart, not just chronological → Train models specifically for agent context compression → Preserve critical decision points while compressing routine chatter → Use different strategies for conversations vs tool outputs 8. Prune context like you're editing a novel → Implement trained pruners that understand relevance, not just recency → Filter based on task relevance while maintaining conversational flow → Adjust pruning aggressiveness based on task complexity 9. Monitor token usage like a hawk → Track exactly where tokens burn in your agent pipeline → Set real-time alerts when context utilization hits dangerous levels → Build dashboards correlating context management with success rates 10. Test everything or admit you're just guessing → A/B test different context strategies and measure performance differences → Create evaluation frameworks testing before/after context engineering changes → Set up continuous feedback loops auto-adjusting context parameters Last but not the least, be open to new ideas and keep learning Check out 50+ AI Agent Tutorials on my profile 👋 .

For years now, prompt engineering shaped how people worked with large language models. It was about finding the right phrasing to get predictable outputs. That approach worked for small tasks, but as models turned into agents that plan, use tools, and retain memory, the limits became obvious. One of Anthropic’s latest articles “𝘌𝘧𝘧𝘦𝘤𝘵𝘪𝘷𝘦 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 𝘧𝘰𝘳 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴”, introduces the next phase in this evolution, called context engineering. It explains that success now depends on how well we manage what goes inside the model’s attention window rather than how we word instructions. Anthropic describes context as everything the model sees while reasoning, including prompts, data, retrieved results, tool outputs, and message history. Every token consumes a portion of the model’s attention, and as the window expands, its focus gradually weakens. The new challenge is to curate that space carefully. Below are the main lessons from Anthropic’s work that stand out for anyone building practical AI systems. 1. Treat context as a limited resource. Adding more information does not improve accuracy. Use only what directly supports the current reasoning step. 2. Write system prompts like structured briefs. Divide them into clear parts for background, instructions, tools, and expected output. 3. Build small, distinct tools. Each tool should solve one problem and return compact, unambiguous results. 4. Use a few canonical examples instead of long lists of edge cases. Examples should teach reasoning, not overwhelm the model with detail. 5. Retrieve data just in time rather than all at once. Lightweight references such as file paths or queries keep the model’s focus clear. 6. Compact long interactions. Summarize the conversation and restart with the essentials so that the model stays coherent over long sessions. 7. Store information outside the context window. Structured notes or state files help maintain continuity across projects. 8. Use sub-agents for large tasks. Specialized agents can work on details while a coordinator manages direction and synthesis. 9. Balance autonomy with reliability. Some data should stay fixed for consistency, while other parts can be fetched dynamically when needed. 10. Focus attention on signal, not volume. Every token should contribute to the next action or decision. Prompt writing will still matter, but the real skill now lies in shaping context and deciding what enters the model, what stays out, and how information evolves as the agent works. The next generation of LLM Agents will depend less on clever wording and more on precise design of memory, retrieval, and context. Context engineering is becoming the foundation for reliable agents that think and act across long horizons with consistency and purpose.

I reverse engineered Claude Claude to figure out what makes it so damn good. Here’s the secret sauce behind the best AI tool in the world & how you can steal it for your own AI workflows: 1. Show Up Briefed Idea: The model works best when it already knows who it’s working for and what success looks like. Steal: Paste a reusable header at the top of every chat — your audience, offer, tone, KPI, and source links. Tell it: “Treat this as context. Ask before writing anything.” 2. Give It Tools, Not Just Prompts Idea: Don’t just chat — connect it to real data. Steal: Let it read from and write to a Google Sheet, Notion page, or API. Define exactly when it should pull info, update it, or ask permission. 3. Plan → Do → Track Idea: Claude manages itself with checklists. Steal: Make it outline a plan before acting, give quick status updates after each step, and add a “REMINDER:” line if it drifts off-track. 4. Split the Work, Then Combine It Idea: Multiple focused chats beat one messy one. Steal: Run 2–3 side chats (market, product, channels). Then use an “Aggregator” prompt to score each idea on impact, confidence, cost, time, and risk — and return one ranked decision with next steps. 5. Remember Rules, Not Rambles Idea: Keep your decisions consistent. Steal: Create a simple `Decisions & Rubrics.md` file. When you change direction, have the model propose a short “diff” — what changed and why. --- Copy-Paste Starters --- 1. Strategy Workshop - use this when you need to design or prioritize your company’s AI roadmap. ``` You are my AI strategy partner. Treat this chat as a working session to design an internal AI roadmap. Context: [Company Name], [Industry], [Team Size], [Core KPI]. Deliverables: (1) 3–5 high-impact AI initiatives ranked by ROI and feasibility, (2) draft 90-day rollout plan. Ask clarifying questions before outputting anything. ``` 2. Content Engine: Use this when you want to turn raw ideas into ready-to-post LinkedIn content. ``` You are my editorial co-pilot. Treat this chat as an always-on content system for LinkedIn. Context: [ICP], [Offer], [Tone], [KPI]. Task: Turn my raw notes into 5 post drafts using the Project OS format (Hook → Insight → Takeaway). After each, ask: “Publish, refine, or queue?” ``` 3. Product Discovery: Use this when you need to extract insights or opportunities from customer research. ``` You are my product research analyst. Goal: Find, cluster, and rank user pain points for [target persona or segment]. Inputs: I’ll paste raw notes or transcript text. Output: A table with columns (Pain Point, Frequency, Impact, Root Cause, Example Quote). After summarizing, suggest 3 potential AI-powered solutions. ``` Most people use ~5% of what AI can do. Don’t just chat with it, run it like an operating system.

When it comes to building truly reliable AI agents, I’ve realized that prompting isn’t just about giving instructions, it’s about crafting intentional conversations that guide the model with clarity, structure, and context. These prompt engineering techniques have shaped the way we should think about deploying LLM-powered systems in the real world. The goal isn’t just output, it’s precision, traceability, and contextual awareness baked into every generation It starts with being hyper-specific and detailed—think of your LLM like a new team member. The clearer you are about their task, constraints, and tone, the better they perform. Pair that with persona prompting to set the right expectations, and suddenly your LLM behaves more like a domain expert than a chatbot. From there, you outline the task and give it a plan, making even the most complex workflows feel digestible for the model. Structuring the prompt with bullet points, Markdown, or even XML-like tags makes the output predictable and parseable, especially when dealing with automation pipelines. I often add few-shot examples directly in the prompt to guide the model with real-world context. These examples anchor behavior and dramatically reduce misunderstanding. Things really start to scale with prompt folding and dynamic generation. In multi-stage flows, I let earlier outputs shape the next prompt. It’s how you make agents more adaptive. Still, I always include an escape hatch—asking the LLM to admit when it doesn't know something. It’s a small tweak that prevents hallucinations and builds trust. For deeper insight, I include debug info or thinking traces. Asking the LLM to explain its logic is like reading the footnotes of its thought process—great for debugging and refinement. But the real crown jewel? Your eval suite. Prompting without evaluation is like flying blind. Having test cases lets you track improvements, regressions, and stability across iterations. Finally, LLM personalities and distillation matter more than people think. Some models need more hand-holding; others just “get it.” I often use a bigger model to refine prompts and then distill them down for faster, cheaper inference with smaller models. Building reliable AI agents, don’t overlook the prompt. Get intentional, get structured.