Common Mistakes In Chatbot NLP Implementation

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?

Your AI chatbot is killing deals. Every day. You spent months implementing it. Trained it on your FAQ database. Deployed it across your website. Now it greets every visitor with enthusiasm. And converts almost none of them. Here's what's actually happening: Your chatbot asks too many questions ↳ Visitors abandon after the third question ↳ Qualification feels like an interrogation ↳ Simple problems become complex conversations It gives generic responses to specific problems ↳ "Our product is great for businesses like yours" ↳ No mention of visitor's actual industry or pain point ↳ Sounds like every other chatbot they've encountered It doesn't know when to shut up ↳ Interrupts visitors trying to browse ↳ Pops up during checkout processes ↳ Triggers at the wrong moments in the buyer journey It can't hand off to humans smoothly ↳ Forces visitors to restart conversations ↳ Loses context when transferring to sales ↳ Creates friction instead of removing it The chatbots converting 15%+ do this differently: They personalize based on visitor behavior ↳ "I see you're looking at our enterprise features" ↳ Reference specific pages or content viewed ↳ Tailor responses to demonstrated interest They ask one perfect question ↳ "What's your biggest challenge with [specific problem]?" ↳ Get visitors talking about pain points ↳ Skip generic qualification scripts They know when to step aside ↳ Silent during checkout processes ↳ Appear only when visitors show confusion signals ↳ Respect the natural buying flow They seamlessly connect to sales ↳ Schedule meetings directly in calendar ↳ Pass full conversation context to humans ↳ Continue the conversation, don't restart it Your conversion fixes: Reduce qualification to one key question. Personalize responses using page context. Time chatbot appearance based on behavior signals. Create smooth handoffs with conversation continuity. Your chatbot should feel like a helpful human. Not a persistent robot. Found this helpful? Follow Arturo Ferreira and repost.

I made a classic mistake while designing a customer support chatbot: I assumed retrieval was “working” just because it returned results. It wasn’t. The model was confidently answering — but using irrelevant context. That’s worse than hallucination, because it looks correct. Here’s where things broke: A user asked: “Where is my order?” The retriever pulled a generic shipping policy instead of the actual order status. The system didn’t fail loudly. It failed convincingly. What I changed I stopped treating retrieval as a black box and fixed it at three levels: 1. Query Understanding (Critical gap) - Split intent: order status ≠ policy question - Added lightweight classification before retrieval 2. Retrieval Quality (Core fix) - Moved from naive keyword search to vector search with better embeddings - Introduced metadata filtering (user_id, order_id) - Top-k wasn’t enough — added re-ranking 3. Grounded Generation (Trust layer) - Forced the model to answer only from retrieved context - If no relevant context → explicit fallback: “I don’t have that information” Result - Wrong but confident answers → dropped significantly - Response relevance → improved immediately - Trust → restored Key realization Retrieval is not a support component in RAG. It is the system. If your retriever is weak, your LLM will fail — just more fluently. Most people try to fix hallucination at the generation layer. That’s the wrong layer. Fix retrieval first. #RAG #GenerativeAI #AIEngineering #LLM #AIArchitecture

After building 20+ AI agents, I've seen the same 4 mistakes destroy otherwise brilliant projects: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 = 𝗔𝗴𝗲𝗻𝘁 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 ❌ Generic ChatGPT prompts won't work for agent instruction ✅ Agent instruction prompt need explicit role definition, tool usage guidelines, and failure handling Pro tip: Include examples of correct tool calling patterns 𝟮. 𝗧𝗼𝗼𝗹 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗦𝘆𝗻𝗱𝗿𝗼𝗺𝗲 ❌ "Let's give our agent access to everything!" ✅ Each unnecessary tool = more hallucinations + higher costs Rule of thumb: Start with 3-5 core tools 𝟯. 𝗪𝗿𝗼𝗻𝗴 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 ❌ Using sequential agents for parallel tasks ✅ Match the pattern to your use case:  • Sequential: Multi-step workflows  • Hierarchical: Complex decision trees  • Cooperative: Real-time collaboration Most fail here because they copy tutorials instead of designing for their specific problem 𝟰. 𝗧𝗵𝗲 "𝗜𝘁 𝗪𝗼𝗿𝗸𝘀 𝗼𝗻 𝗠𝘆 𝗠𝗮𝗰𝗵𝗶𝗻𝗲" 𝗧𝗿𝗮𝗽 ❌ Skipping evals, security, and monitoring ✅ Production-ready means: - Automated evaluation metrics - Content filtering, Prompt Injection protection - Real-time observability and Monitoring The hard truth: 98% of AI POCs never make it to production. The reason? Teams focus on the "chatbot" demo without considering production architecture 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀? 𝗜 𝘀𝗵𝗮𝗿𝗲 𝘄𝗲𝗲𝗸𝗹𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗼𝗻 𝗔𝗜 𝗼𝗻 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗮𝗻𝗱 𝗼𝗻 𝗺𝘆 𝗯𝗹𝗼𝗴𝘀. #AIAgents #MachineLearning #AI #LLMs #GenAI #ProductionAI #TechLeadership #PromptEngineering #SoftwareDevelopment #AIStrategy

𝐒𝐭𝐨𝐩 𝐔𝐬𝐢𝐧𝐠 𝐋𝐋𝐌𝐬 𝐋𝐢𝐤𝐞 𝐓𝐡𝐢𝐬 🚫 Most teams don’t fail because they didn’t use LLMs… They fail because they used them the wrong way. If you're building anything with GenAI — a chatbot, internal assistant, automation tool, or RAG app — these are the 9 mistakes that quietly destroy quality, trust, and user experience: ❌ 1) Zero-shot prompts for complex tasks ✅ Use few-shot examples for clarity ❌ 2) Monolithic prompting (everything in one huge prompt) ✅ Use prompt chaining (smaller steps) ❌ 3) Treating LLMs like databases ✅ Use RAG + verified sources ❌ 4) Ignoring latency ✅ Stream responses + cache + show progress ❌ 5) Overkill with big models ✅ Right-size models based on complexity ❌ 6) Temperature misuse ✅ Tune temperature intentionally (accuracy vs creativity) ❌ 7) No guardrails ✅ Add input moderation + system rules + output filtering ❌ 8) No feedback loops ✅ Track responses + collect ratings + continuously improve ❌ 9) Using LLMs for strict logic tasks ✅ Combine LLMs with deterministic code 🎯 Key takeaway: Stop treating LLMs like magic boxes. Smart usage = better results + lower cost + happier users. If you’re building AI products right now, save this and share it with your team. 🔁 Repost for your network ♻️ Follow Me for more such useful resources #GenerativeAI #LLMs #PromptEngineering #RAG #AIProducts #AIEngineering #MachineLearning #ArtificialIntelligence

17 mistakes SaaS founders make when building conversational UX (and how to avoid them) In the last year, we’ve worked on 20+ conversational AI products sales copilots, analytics assistants, onboarding bots & finance copilots. Here’s what we’ve learned the hard way. Advice I wish more founders followed before shipping: 1. Don’t bury intent under “Hello.” Users don’t want greetings; they want to act. Show the top 3 actions the AI can drive on day one. 2. Design the first task, not the first conversation. “Hi, how can I help?” is lazy. Instead: “Want me to summarize your last 5 calls?” 3. Inline actions beat endless replies. Let users click “Approve invoice” inside chat, instead of typing “yes” every time. 4. Error states are opportunities. A failed query can still teach the user how to ask better, if you design recovery flows. 5. Compress multi-turn flows. If a user has to go through 5 clarifications to schedule a meeting, the bot failed. 6. Make memory visible. Users trust “I’ve saved your filter for next time” more than invisible black-box recall. 7. Surface system confidence. “I’m 70% sure this matches your request — want to double-check?” feels safer than silent hallucination. 8. Always preview before commit. Let users see the draft email, the SQL query, or the update before execution. 9. Conversation ≠ control. When actions are destructive (delete, send, publish), always offer a button, not a text command. 10. Don’t hide behind personality. Users forgive bland tone, not incorrect results. Utility first, warmth second. 11. Expose what’s possible. 80% of users under-use bots because they don’t know the boundaries. UX = setting expectations. 12. Micro-interactions matter. Typing indicators, inline loaders, partial responses , all build trust during delay. 13. Design “I don’t know” gracefully. Redirect to documentation, dashboard, or human, don’t leave users in dead air. 14. Conversation must map to SaaS jobs. Your AI should be judged by how fast it moves users to business outcomes. 15. Differentiate explore vs execute. Browsing analytics needs open prompts; approving payments needs guardrails. 16. Measure beyond CSAT. Track “time to outcome” or “tasks completed per session.” That’s the real UX KPI for bots. 17. End strong. Every session should close with clarity: “Task done. Next step?”. We’ve seen SaaS teams polish UIs for months, but lose users in the first 2 messages of their conversation. The real UX work is in trust, recovery, and clarity. If you’re building a conversational AI or SaaS product and want to avoid these mistakes: At Bricx, you don’t just get a senior designer embedded in your team, you also get a design lead, PM, and brand designer on call. Book a call to integrate some kickass design partners to your team. 

𝐓𝐨𝐩 5 𝐌𝐢𝐬𝐭𝐚𝐤𝐞𝐬 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐌𝐚𝐤𝐞 𝐖𝐡𝐞𝐧 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 🚫 Most companies fail at AI agent implementation. Not because of bad tech — but bad strategy. After delivering AI-powered solutions for startups, public sector teams, and enterprise clients like Hitachi and Nissan-Infiniti, here are the top 5 mistakes we see — and how to fix them before it's too late: 1. 𝐉𝐮𝐦𝐩𝐢𝐧𝐠 𝐢𝐧 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐚 𝐂𝐥𝐞𝐚𝐫 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞 𝐌𝐢𝐬𝐭𝐚𝐤𝐞: “Let’s just add a chatbot.” 𝐅𝐢𝐱: Start with one workflow where AI can clearly save time or boost efficiency (support, onboarding, lead capture). 2. 𝐔𝐬𝐢𝐧𝐠 𝐆𝐞𝐧𝐞𝐫𝐢𝐜 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐌𝐢𝐬𝐭𝐚𝐤𝐞: Plug-and-play bots that don’t understand your business. 𝐅𝐢𝐱: Fine-tune models with your internal data — Agentic AI needs deep context to drive real value. 3. 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 & 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐌𝐢𝐬𝐭𝐚𝐤𝐞: Collecting user data without clarity or control. 𝐅𝐢𝐱: Use XAI (Explainable AI) approaches and follow GDPR/industry compliance from day one. 4. 𝐒𝐤𝐢𝐩𝐩𝐢𝐧𝐠 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐌𝐢𝐬𝐭𝐚𝐤𝐞: Launching the bot and forgetting about it. 𝐅𝐢𝐱: AI agents must learn, improve, and adapt — treat it like a product, not a feature. 5. 𝐄𝐱𝐩𝐞𝐜𝐭𝐢𝐧𝐠 𝐈𝐧𝐬𝐭𝐚𝐧𝐭 𝐑𝐎𝐈 𝐌𝐢𝐬𝐭𝐚𝐤𝐞: Giving up when results aren’t immediate. 𝐅𝐢𝐱: Track the right KPIs (like reduced support hours, increased lead engagement) over 30–90 days. 🎁 𝐖𝐚𝐧𝐭 𝐨𝐮𝐫 𝐟𝐫𝐞𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐂𝐡𝐞𝐜𝐤𝐥𝐢𝐬𝐭? Comment or Inbox “𝐀𝐈 𝐑𝐄𝐀𝐃𝐘” and I’ll send it directly to you. 💬 What mistake do you think is most common? Let’s talk about it in the comments. #ai #agenticai #genai #xai #chatbot #automation #businessgrowth #shadhinlab #aiagent #agent

How to avoid burning $10,000 a day building a bad corporate chatbot 😬 🤖 . So many corporates are diving headfirst into building chatbots. Ones for their marketing teams to generate content, ones for their executives to ask about data, ones for HR policies. Are they good? Some of them. But a lot of them are a massive disappointment. 😑 You are basically setting money on fire if you make these 5 errors. So here’s how to avoid building a Clippy sequel: 👉 Mistake #1: Diving into Prompt-Crafting First It's tempting to jump right into making prompts because it feels like progress. But that's like framing a house without a plan. First, figure out what your chatbot needs to do. Identify its purpose with use cases and user stories. This sets a solid foundation for your developers. 👉 Mistake #2: Building a Super Bot When you try to make your chatbot handle everything at once, it gets overwhelmed. Think of breaking down tasks into bite-sized pieces and maybe even consider multiple specialized bots. Tools like LangChain can help string together the right tools based on the query. 👉 Mistake #3: Trusting GenAI with Math There’s girl math, boy math, and then GenAI math, where it straight-up makes stuff up. If you give it data analysis to do, it will only be correct about 80% of the time (anecdotal, but still, it's bad). Stick to reliable tools like Python for number crunching, and keep GenAI for what it does best (creativity). 👉 Mistake #4: Overloading Your Bot with Data Dumping all your data on the bot without context is a recipe for chaos. I keep hearing people say “let’s just give it all the data.” No. Just no. Build a Data Glossary to give it a cheat sheet of your business’s lingo and data. This helps the bot understand your world better. 👉 Mistake #5: Not communicating Users will have no idea what your chatbot can do. Make sure the first interaction is proactive and the bot is explaining itself and what it is capable of. Set those expectations! If a user is disappointed from the first try, they tend not to come back. There you have it, your crash course in how to avoid building a digital tumbleweed. Share your worst chatbot experiences please. I need some humor in my life! 😂

What takes 70% of my time in chatbot development? Getting rid of its worst nightmare: Bad knowledge base behind. In this case, chatbot is using knowledge, which is: - Poorly written; - Unstructured; - Multilingual; - Multi-format; - Best part: Contains mutually exclusive paragraphs. (a lot of them!). Using embeddings? Get even more chaos. Most companies have data/knowledge stored in that way: A bunch of internal and public web pages, PDF, Word, and Excel docs, from different times and versions, in multiple storages and, sometimes (especially in the EU) - written in different languages. What happens when all of that (without pre-moderation) goes into the chatbot context? In the best case - generic answers. In the worst case - completely wrong answers. A good old rule of AI implementation still applies: Garbage in - garbage out. So if the knowledge isn't cleaned - a whole chatbot thing goes wrong in terms of accuracy. So, clean it first: 1) Get rid of redundant and outdated docs; 2) Check what is left for consistency; 3) Fix inconsistencies; 4) Bring everything into the same text format; 5) Translate into the same language (and check it afterward); 6) Spend some time to clean numeric data, if you have it; 7) Bring your pieces of knowledge for the chatbot into a separate repository; 8) Finally, connect it to the chatbot as a source. It'll get you a much more accurate and relevant chatbot than anything with "just a bunch of uploaded PDFs and web pages". Boring, time-consuming, but totally worth it. Give it a try. #Chatbot #ConversationalAI #LLM #CustomerSupport