Tips for Ensuring Chatbot Accuracy

Article from NY Times: More than two years after ChatGPT's introduction, organizations and individuals are using AI systems for an increasingly wide range of tasks. However, ensuring these systems provide accurate information remains an unsolved challenge. Surprisingly, the newest and most powerful "reasoning systems" from companies like OpenAI, Google, and Chinese startup DeepSeek are generating more errors rather than fewer. While their mathematical abilities have improved, their factual reliability has declined, with hallucination rates higher in certain tests. The root of this problem lies in how modern AI systems function. They learn by analyzing enormous amounts of digital data and use mathematical probabilities to predict the best response, rather than following strict human-defined rules about truth. As Amr Awadallah, CEO of Vectara and former Google executive, explained: "Despite our best efforts, they will always hallucinate. That will never go away." This persistent limitation raises concerns about reliability as these systems become increasingly integrated into business operations and everyday tasks. 6 Practical Tips for Ensuring AI Accuracy 1) Always cross-check every key fact, name, number, quote, and date from AI-generated content against multiple reliable sources before accepting it as true. 2) Be skeptical of implausible claims and consider switching tools if an AI consistently produces outlandish or suspicious information. 3) Use specialized fact-checking tools to efficiently verify claims without having to conduct extensive research yourself. 4) Consult subject matter experts for specialized topics where AI may lack nuanced understanding, especially in fields like medicine, law, or engineering. 5) Remember that AI tools cannot really distinguish truth from fiction and rely on training data that may be outdated or contain inaccuracies. 6)Always perform a final human review of AI-generated content to catch spelling errors, confusing wording, and any remaining factual inaccuracies. https://lnkd.in/gqrXWtQZ

You’re in an AI engineer interview. Interviewer: Your RAG chatbot starts giving outdated answers as documents change daily. How would you keep it fresh without reprocessing everything? If your documents change but your embeddings don’t, your system is already outdated. Here’s how you fix that in a production setup: 1. Don’t rebuild - detect change Track updates using timestamps, checksums, or versioning. Only reprocess what actually changed instead of re-indexing everything. 2. Go chunk-level, not document-level If a small section changes, update only those chunks. This keeps updates fast, cheap, and scalable. 3. Event-driven ingestion (real-time freshness) Use Apache Kafka to capture document update events in real time. How it helps: 📍Every document change becomes an event (no missed updates) 📍Consumers automatically trigger parsing + embedding pipelines 📍Decouples your system -> ingestion scales independently from updates Result: your RAG system stays continuously updated, not batch-dependent. 4. Clean your vector store actively Use upserts and deletions to replace outdated embeddings. Otherwise, stale chunks will still show up during retrieval. 5. Make retrieval freshness-aware Store metadata like last_updated or version. Filter or boost recent chunks so the model sees the latest information first. 6. Cache carefully Include document version or timestamp in cache keys. Without this, you’ll serve fast but outdated answers. 7. Add observability (this is where most systems fail silently) Use MLflow to trace your entire pipeline. How it helps: 📍Track which document version and chunks were retrieved per query 📍Monitor when embeddings were last updated 📍Debug issues like stale retrieval or hallucination despite fresh data Result: you don’t just update data, you prove your system is using the latest data. #ai #llm #datascience #rag #chatbot #aiengineering #kafka #mlflow #interview Follow Sneha Vijaykumar for more...😊

I didn't ship a chatbot. I shipped a state machine. The prompt didn't fail. The memory routing did. Three weeks into building the vendor onboarding agent, I hit a hard wall. The flow looked clean in my notebook: ingest to extract to push to HubSpot. In production? It hallucinated compliance flags. Skipped signature blocks. Retried without context. I was treating the LLM like control flow. It's not. It's a stochastic function. You need explicit state. So I rebuilt it as a directed graph in LangGraph: 🔹 Ingest Node: PDF to chunking to semantic embedding to Pinecone for episodic memory storage 🔹 Extract Node: ReAct planner with Pydantic schema validation. If confidence drops below 0.82, route to fallback. 🔹 Validate Node: Guardrails AI plus regex plus business rule engine. Catches hallucinated tags before they hit the CRM. 🔹 Router Node: State-based conditional edges. New vendor triggers full flow. Existing vendor triggers delta update. Ambiguous input routes to HITL queue. The breakthrough wasn't better prompts. It was state management. I stopped asking the model to remember. I gave it a shared State object passed between nodes. I stopped hoping for accuracy. I set confidence thresholds, built semantic similarity checks, and routed failures to humans instead of guessing. I stopped tracking tokens. I tracked latency per node, cache hit rates, and fallback frequency. What changed in production: - Task completion: 62% to 89% - Hallucination rate: 0.3%, caught pre-CRM - HITL intervention: 38% to 12% - Cost per qualified record: $4.20 Agents aren't conversational UIs. They're state machines with stochastic components. If you're building GTM agents, stop optimizing for flow. Start optimizing for: ✅ Explicit state transitions, not chain-of-thought ✅ Confidence-based routing, not open-ended retries ✅ Observability traces via LangSmith or Phoenix, not open rates ✅ Human-in-the-loop as a fallback node, not an afterthought What's the hardest state transition you've had to engineer in your agent workflows? Drop your stack below. 👇 #AIGTM #LangGraph #MultiAgent #PMM #SystemDesign #AIEngineering #RAG #HITL #FieldNotes

Most people write prompts. Very few actually design them. And that difference is exactly why some people get average AI outputs, while others get precise, high-quality results every single time. A well-structured prompt is not just an instruction. It’s a system. Here’s a simple but powerful framework to improve how you work with AI: 1. Start with clarity, not commands    “I want to [TASK] so that [SUCCESS CRITERIA].”    If success is vague, the output will be inconsistent. Define the outcome before anything else. 2. Provide strong context    Attach relevant files, background, or data.    AI performs significantly better when it understands the full picture rather than isolated instructions. 3. Use references instead of guesswork    Show what “good” looks like. Whether it’s tone, structure, or style—reference-driven prompting removes ambiguity and improves consistency. 4. Create a clear Success Brief - Type of output (post, report, proposal, etc.) - Desired audience reaction (what they should think, feel, or do) - What it should not sound like - What success actually means (approval, response, action) This step alone separates average outputs from professional-grade results. 5. Define rules and constraints    Set boundaries clearly.    If something should not be done, state it explicitly. Constraints guide quality just as much as instructions. 6. Align before execution    Don’t rush into output generation. Ask the AI to clarify assumptions, identify key rules, and propose a plan before starting. This avoids rework and improves accuracy. The real shift is this: Stop treating AI like a tool that follows commands. Start treating it like a collaborator that needs a structured brief. When you do that, the quality of output changes dramatically. #AI #PromptEngineering #ChatGPT 

Most people prompt ChatGPT like it’s a search engine. But the pros do something different. They use a method called Self-Ask Prompting—a simple but powerful technique that improves answer accuracy by breaking down the task before solving it. It’s been shown to cut hallucinations nearly in half, from 40% to 17%. I’ve started using it for research, content development, and strategy prompts—and the quality of output is significantly higher. Here’s the structure: 1. Instruct ChatGPT to decompose the task before answering. 2. Let it ask any follow-up questions it needs. 3. Have it loop until all clarifications are handled. 4. Then deliver the final answer using only the information generated. 5. End with a confidence score. You can copy and paste this exact format into ChatGPT: You must decompose the task before answering. Question: <YOUR ACTUAL INPUT = YOUR PROMPT HERE> Step 1 – Need follow-up questions? Answer Yes/No. If Yes, loop: Follow-up #: <leave blank — you (ChatGPT) write the clarifying question> Answer #: (Repeat until no further follow-ups are needed.) Step 2 – Final output: Use only the facts in Answer lines. If key info is missing, say: “Insufficient information.” End with a 0–100% confidence score. It takes less than a minute to set up—but dramatically improves the quality of what you get back. If you rely on AI for serious work, this is worth testing. #AI

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.

A new paper by Lucy Osler reframes “AI hallucinations” in a way most teams miss. We often here the risk as "hallucinations". But she posits the risk is shared belief-making. Osler uses distributed cognition to describe a shift. From “AI hallucinates at you.” To “you hallucinate with AI.” How does this apply to your workflows? Chatbots sit inside thinking, memory, planning, and self-narration. Chatbots speak in a social voice, so replies feel like validation from an “other.” Validation turns a private belief into a shared reality fast. Concrete examples from the paper- A Replika companion affirmed Jaswant Singh Chail’s self-story as a “Sith assassin” and treated an assassination plan as “viable,” according to court records cited in the paper. A lawyer filed fabricated citations after using ChatGPT for legal research in Mata v. Avianca. Google Search AI recommended glue on pizza during the AI Overviews rollout. What to do in your org this week- Write one rule for high-stakes use. No chatbot use for legal filings, medical guidance, self-harm content, violence planning, or crisis counseling. Route those cases to a human professional. Add friction on purpose. For any decision memo, require two sources outside chat. One primary source, one domain expert. Ban “validation prompts” in sensitive areas. Remove prompts like “tell me I am right,” “confirm my theory,” “help me prove,” when the topic involves paranoia, conspiracy, grievance, or identity crisis. Teach a one-line self-check for staff. “Am I asking for truth, or am I asking for agreement.” Turn off memory features for work accounts unless a use case demands them. If memory stays on, add a review habit. Weekly audit of saved facts and profile claims. Train against sycophancy. Tell teams to ask for disconfirming evidence first. “List reasons this claim fails.” “What would change your answer.” If you build products- Treat conversational tone as a safety surface, not a style choice. Add refusal patterns for delusion reinforcement. Detect spirals around “secret missions,” “divine messages,” “the matrix,” “hidden inheritance,” and similar scripts. Log and review “agreeable escalation.” Watch for sessions where the model moves from polite support to active endorsement. You do not need to panic. You need guardrails where belief gets made. https://lnkd.in/gdFnaYti

Most people are using GenAI wrong. They ask one-shot questions and expect magic. If you want real results, that are more relevant, thoughtful, and useful, then you need to prompt better. Here are two advanced prompting patterns that dramatically improve output from any major GenAI chatbot (ChatGPT, Claude, Gemini, Copilot, etc.). These patterns work across them all. CHAIN-OF-THOUGHT PATTERN - Get the model to “think out loud” by breaking down its reasoning into clear, logical steps before giving an answer. Use cases: math, logic, pricing, diagnostics, and planning. Steps: * Use cues like “Let’s work this out step by step." * Optionally include an example (few-shot) or let it figure it out (zero-shot). ✔️ Pros: Improves accuracy and transparency. ❌ Cons: Slower, and if the first step is wrong, the rest often is. TREE-OF-THOUGHT PATTERN - Structure your prompt so the model explores multiple paths or ideas, then compares and converges on the best option. Use cases: root cause analysis, strategic decisions, and product ideas. Steps: * Ask it to explore different possibilities. * Have it compare them. * Ask for a final recommendation. ✔️ Pros: Encourages critical thinking and creativity. ❌ Cons: Verbose, computationally heavy, may overthink. Most people stop at the first answer. These techniques push the model to do more: to reason, refine, and iterate. Prompt smarter. Get better results. #PromptEngineering #GenerativeAI #ChatGPT #AIProductivity #WorkSmarter #AdvancedPrompts #AIChatbots #LLMs #AIForWork

💡 Three Powerful Prompts to Enhance Your AI Interactions 💡 I’ve been exploring different ways to get the most out of AI tools like ChatGPT, and these are my current favorite prompts: 🔗 Chain of Thought (CoT) GenAI models already use Chain of Thought reasoning internally, but I explicitly prompt for it to make the AI’s thought process transparent. This way, I can review each step, refine it if needed, and ensure accuracy. 📌 Example: “Using CoT, how would you design an activity for …?” The AI will break down its reasoning step by step. If I’m happy with the process, I’ll then follow up with: “Now design that activity.” 🌳 Tree of Thought (ToT) ToT is invaluable when I want the AI to consider multiple perspectives or reference specific sources. This approach helps ensure it pulls from relevant knowledge rather than generating a generic response. 📌 Example: “Using ToT, who are the experts connected to the Doppler theory? Please provide their names and bios.” By verifying the bios first, I can confirm ChatGPT has accurate information before asking it to synthesize a summary based on those experts’ perspectives. ❓ Ask Me Questions If you had a human assistant, they’d likely ask clarifying questions before completing a task. Yet, we don’t often allow AI the same opportunity. A simple but effective strategy is to invite ChatGPT to ask questions before generating a response. 📌 Example: After making a request, add: “Do you have any questions that would help you perform this task better?” The AI will then ask for missing details, and by providing brief answers, you’ll get a far more precise and useful response. These simple tweaks have significantly improved the quality of AI-generated content for me. Give them a try and let me know how they work for you! 🚀

JOW I BUILT A CHATBOT IN SALESMATE (01) Yesterday I mentioned I was going to start sharing the detailed steps on how I built a chatbot in Salesmate for an Australian company. Since the chatbot was built for a printing business, the first thing I focused on was structure before conversation. Printing workflows involve pricing variations, custom jobs, delivery timelines, and edge cases. That means the chatbot cannot guess or give loose answers. I started with the AI Pilot setup inside Salesmate. This is where I defined how the chatbot should behave before it ever interacts with a real visitor. I set the response tone, connected the correct knowledge base, and added guardrails so the chatbot stays consistent and controlled. Next, I worked on the prompts. I intentionally limited this to three core prompts. •A greeting prompt to set expectations at the start of the conversation. •A fallback prompt for situations where the visitor’s message is unclear or incomplete. •An escalation prompt for moments where the chatbot should stop and hand the conversation over to a human. I chose these three because they cover most real interactions. Visitors either start a conversation, ask something unclear, or reach a point where human assistance is needed. Adding more prompts at this stage often creates confusion instead of clarity. With the foundation in place, I moved on to intents. Salesmate allows you to define intents as clear goals rather than open-ended conversations. I used this to separate customer requests into specific paths such as pricing and quotes, product information, order tracking, reorders, returns, and shipping. This separation is important. When everything is treated as a single conversation, systems become difficult to manage. By defining clear intents, the chatbot understands what type of request it is handling and what it is allowed to do next. This setup phase is not the most visible part of building a chatbot, but it is the part that determines whether it works properly when real customers start using it. Tomorrow, I’ll break down how I handled entities, functions, variations, and agent instructions to make these intents work reliably with real user input. If you are using Salesmate or Shopify and considering a structured chatbot setup, feel free to reach out. Happy to share how this approach works in practice. #Salesmate #chatbot #shopify #automation #AI #letsconnect #linkedin