Natural Language Processing For Chatbots

What’s missing in conversational AI? The ability to plan responses across turns strategically to achieve goals. Most conversational AIs: • Focus on single responses • Lack strategic, long-term goals • Miss out on real human connection New UC Berkeley publications are contributing to the game: 𝗤-𝗦𝗙𝗧 (Q-Learning via Supervised Fine-Tuning) • Adapts Q-learning to train language models • Adds long-term planning directly into responses • Helps AIs respond with strategy, not just reaction 𝗛𝗶𝗻𝗱𝘀𝗶𝗴𝗵𝘁 𝗥𝗲𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 • Replays past conversations to find better responses • Learns from the past to improve future replies • Guide smarter conversational strategies Applications? • 𝗠𝗲𝗻𝘁𝗮𝗹 𝗛𝗲𝗮𝗹𝘁𝗵 𝗦𝘂𝗽𝗽𝗼𝗿𝘁: Builds trust, helping users feel heard. • 𝗘-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲: Remembers past chats to close sales. • 𝗖𝗵𝗮𝗿𝗶𝘁𝘆: Guides conversations with empathy, boosting donations. Together, these methods will allow CAI to be goal-oriented, plan strategically, adapt, and connect with users.

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'm now spending around 40-50% of my time with clients on AI. Polishing prompts, setting up workflows. Here's the top 3 most common mistakes I see: 1. Trying to provide too much information in the context window. What's too much? 𝗥𝗲𝗱𝘂𝗻𝗱𝗮𝗻𝘁 𝗰𝗼𝗻𝘁𝗲𝗻𝘁: Repeating the same information multiple times or including verbose explanations that could be summarised. 𝗜𝗿𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗱𝗲𝘁𝗮𝗶𝗹𝘀: Information unrelated to the task at hand that dilutes what's important. 𝗘𝘅𝗰𝗲𝘀𝘀𝗶𝘃𝗲 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Providing 10+ examples when 2-3 would sufficiently illustrate the concept 𝗨𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝘂𝗺𝗽𝘀: Large blocks of unformatted text, logs, or data without clear organisation 𝗙𝘂𝗹𝗹 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝘀 𝘄𝗵𝗲𝗻 𝗲𝘅𝗰𝗲𝗿𝗽𝘁𝘀 𝘀𝘂𝗳𝗳𝗶𝗰𝗲: Including entire papers or articles when only specific sections are relevant 𝗞𝗲𝘆 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀 𝘆𝗼𝘂'𝘃𝗲 𝗵𝗶𝘁 "𝘁𝗼𝗼 𝗺𝘂𝗰𝗵": • The model struggles to find relevant details buried in noise • Response quality degrades due to information overload • Important instructions get lost in the volume 2. Being either too loose or too prescriptive. Some clients operate within rigid systems (like optimising for pre-defined feeds or API outputs). So they don't understand that large language models operate best when provided with - natural language examples. On the too loose spectrum: • "Be helpful and accurate" (no specifics on HOW) • "Write in a professional tone" (what does professional mean?) • "Keep responses appropriate length" (what's appropriate?) • No examples of desired outputs • Vague quality criteria 3. Asking the AI to see the future. Not understanding that the AI is drawing on what's readily available in it's dataset. That being everything it's ingested on the internet. It isn't 'thinking' and able to come up with innovative solutions to niche areas it has little context on. Which ones I did miss?

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?

Stop blaming ChatGPT, Claude , or Grok for bad outputs when you're using it wrong. Here's the brutal truth: 90% of people fail at AI because they confuse prompt engineering with context engineering. They're different skills. And mixing them up kills your results. The confusion is real: People write perfect prompts but get terrible outputs. Then blame the AI. Plot twist: Your prompt was fine. Your context was garbage. Here's the breakdown: PROMPT ENGINEERING = The Ask CONTEXT ENGINEERING = The Setup Simple example: ❌ Bad Context + Good Prompt: "Write a professional email to increase our Q4 sales by 15% targeting enterprise clients with personalized messaging and clear CTAs." AI gives generic corporate fluff because it has zero context about your business. ✅ Good Context + Good Prompt: "You're our sales director. We're a SaaS company selling project management tools. Our Q4 goal is 15% growth. Our main competitors are Monday.com and Asana. Our ideal clients are 50-500 employee companies struggling with team coordination. Previous successful emails mentioned time-saving benefits and included customer success metrics. Now write a professional email to increase our Q4 sales by 15% targeting enterprise clients with personalized messaging and clear CTAs." Same prompt. Different universe of output quality. Why people get this wrong: They treat AI like Google search. Fire off questions. Expect magic. But AI isn't a search engine. It's a conversation partner that needs background. The pattern:  • Set context ONCE at conversation start • Engineer prompts for each specific task  • Build on previous context throughout the chat Context Engineering mistakes:  • Starting fresh every conversation  • No industry/role background provided  • Missing company/project details • Zero examples of desired output Prompt Engineering mistakes:  • Vague requests: "Make this better" • No format specifications  • Missing success criteria • No tone/style guidance The game-changer: Master both. Context sets the stage. Prompts direct the performance. Quick test: If you're explaining your business/situation in every single prompt, you're doing context engineering wrong. If your outputs feel generic despite detailed requests, you're doing prompt engineering wrong. Bottom line: Stop blaming the AI. Start mastering the inputs. Great context + great prompts = consistently great outputs. The AI was never the problem. Your approach was. #AI #PromptEngineering #ContextEngineering #ChatGPT #Claude #Productivity #AIStrategy Which one have you been missing? Context or prompts? Share your biggest AI struggle below.

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.

The Harsh Reality of Building a Production-Ready RAG Pipeline Building an AI chatbot with a RAG pipeline sounds simple—just watch a few YouTube tutorials, throw in an off-the-shelf LLM API, and boom, you have your own AI assistant. But anyone who has ventured beyond the tutorials knows that a real-world, production-level RAG pipeline is a completely different beast. It’s almost a month into my journey at LLE, where I’ve been working on developing an in-house RAG pipeline using foundational models—not just for efficiency but also to prevent data breaches and ensure enterprise-grade robustness. And let me tell you, the challenges are far from what the simplified tutorials portray. A Few Hard-Hitting Lessons I’ve Learned: ✅ Chunking is not just splitting text You can use pymupdf to extract chunks, but it fails when you need adaptive chunking—especially for scientific documents where preserving tables, equations, and formatting is critical. This is where Visual Transformer models that performs an Optical Character Recognition (OCR) task for processing scientific documents into a markup language comes into play. ✅ Query Refinement is Everything A chatbot is only as good as the data it retrieves. Rewriting follow-up queries effectively is key to ensuring the LLM understands intent correctly. Precision in query structuring directly impacts retrieval efficiency and model response quality. ✅ Optimizing Retrieval = Speed + Relevance It's not just about retrieving data faster; it’s about retrieving the right data. Reducing chunks improves retrieval efficiency, but that’s not enough—multi-tiered storage strategies ensure queries target the right system for lightning-fast and relevant responses. These are just a few of the many challenges that separate a toy RAG implementation from a real-world, scalable, and secure pipeline. The deeper I dive, the clearer it becomes: production-ready AI isn’t just about making things work, it’s about making them work at scale, securely, and efficiently. Would love to hear from others working in this space—what are some of the biggest roadblocks you’ve faced while building a RAG pipeline? 🚀

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 👋 .