Best Practices for Chatbot Implementation

Interviewer: "Your AI chatbot receives 1 million requests every day. How would you reduce LLM costs without affecting answer quality?" Answer: The first thing I would not do is switch to a smaller model. Cost optimization should happen across the entire pipeline, not just at the model layer. 1. Introduce Semantic Caching Many users ask the same question in different ways. For example: - "What's your refund policy?" - "Can I get my money back?" - "How do refunds work?" Although the wording is different, the intent is the same. By storing embeddings of previous queries, we can retrieve a previously generated answer when a new query is semantically similar, avoiding another LLM call. Tools: Redis + vector search, FAISS, Qdrant, Pinecone. 2. Use Prompt Caching A large portion of prompts often contains static content such as: - System instructions - Company policies - Tool descriptions - RAG instructions Instead of sending these repeatedly, use provider-supported prompt caching where available. This reduces both input tokens and latency. 3. Multi-Model Routing Not every request requires the most expensive model. For example: - FAQs → Small LLM (Llama 3.1 8B, Gemma) - Summarization → Medium model - Complex reasoning or coding → GPT-5, Claude, or another high-end model A lightweight classifier or router can determine which model should handle each request. 4. Improve Retrieval Before Generation If you're using RAG, better retrieval means the LLM receives cleaner context. Focus on: - Better chunking - Hybrid search - Cross-encoder reranking - Query rewriting - Metadata filtering When the context is highly relevant, even smaller models can produce excellent answers. 5. Reduce Token Usage Every token costs money. Optimize by: - Compressing retrieved context - Removing duplicate chunks - Retrieving only the Top-K relevant documents - Limiting conversation history - Summarizing long chat histories instead of sending everything Reducing unnecessary tokens lowers both cost and response time. 6. Batch Non-Real-Time Requests Tasks such as document summarization, report generation, or data extraction don't always need immediate responses. Batching these requests improves throughput and reduces infrastructure costs. 7. Fine-Tune Small Models for Repetitive Tasks If a task is highly repetitive, such as intent classification, entity extraction, or support categorization, a fine-tuned smaller model can replace a large general-purpose LLM. This improves both speed and cost efficiency. 8. Continuously Monitor Cost and Quality Optimization is an ongoing process. Track metrics such as: - Cost per request - Token consumption - Cache hit rate - Latency - Model routing distribution - User satisfaction - Task success rate The goal is to reduce cost without degrading answer quality. Follow Sneha Vijaykumar for more...😊 #ai #llm #rag #aiengineer #interview #preparation #datascience

Most people who are building agents for the first time get stuck. Because what they read is too vague, too technical, or too hyped. If you want to build one, here's a process that works. This isn't theory. It's the same process I use every time I ship a working agent. 1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝘀𝗺𝗮𝗹𝗹 𝗮𝗻𝗱 𝗰𝗹𝗲𝗮𝗿 𝗷𝗼𝗯 Most people start with something big. "I want an AI assistant." That's not a job. That's a goal. A job is one task, on one day, with one output. Sort my Monday inbox into reply, archive, and defer. Most importantly, define what "done" looks like in a simple sentence. 2. 𝗖𝗵𝗼𝗼𝘀𝗲 𝘆𝗼𝘂𝗿 𝗟𝗟𝗠 Most first-timers worry about fine-tuning or compare benchmarks. Don't. Just pick a frontier LLM (Claude, GPT, Gemini) and move on. You need two things from the model: it should reason well, and it should return clean JSON. And frontier models do both. 3. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘄𝗵𝗮𝘁 (𝗮𝗰𝘁𝗶𝗼𝗻𝘀) 𝗶𝘁 𝗰𝗮𝗻 𝗱𝗼 Don't skip this step. A chatbot talks. An agent takes "action". It does that with 𝘵𝘰𝘰𝘭𝘴 — APIs or actions it can call. Pick the smallest set of tools that gets the job done. Web search (Brave). Email (Gmail). Calendar (Google). Two tools is plenty for v1. Four is already a lot. 4. 𝗖𝗿𝗲𝗮𝘁𝗲 𝘁𝗵𝗲 𝗯𝗮𝘀𝗶𝗰 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 Skip third party tools like LangGraph, CrewAI, Mastra. They're good. But they're the wrong starting point. Build the loop yourself first. User sends a task → you pass it to the model → model reads the system prompt → model decides → calls a tool → shares an answer → feed the answer back → Loop until done. 5. 𝗔𝗱𝗱 𝗺𝗲𝗺𝗼𝗿𝘆 𝗯𝘂𝘁 𝗱𝗼𝗻'𝘁 𝗼𝘃𝗲𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 No need for a vector database on day one. Most v1 agents work fine with the last few messages (short term memory). Need to remember between runs? Save it to a SQLite row or a JSON file. One line of code. 6. 𝗕𝘂𝗶𝗹𝗱 𝗮𝗻 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝘆𝗼𝘂 𝗹𝗶𝗸𝗲 The terminal is fine for the first few runs. Then you need to actually use the agent, not test it. Put it somewhere you'll use every day. A web UI on Streamlit. A Slack bot. A cron job that emails you the output. 7. 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 𝗳𝗮𝘀𝘁 𝗮𝗻𝗱 𝗾𝘂𝗶𝗰𝗸 The first run will (read: should) be embarrassing. The second will be less embarrassing. Run real tasks. Log every failure in a text file. Fix whatever is broken -- the prompt, the tool, the loop. Run again. Every working agent needs to go through dozens of these cycles. 8. 𝗕𝗲 𝗿𝗲𝗹𝗲𝗻𝘁𝗹𝗲𝘀𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝘀𝗰𝗼𝗽𝗲 Once it works, the instinct is to add more tools. Don't. Every new tool is a new way the agent can break. A boring agent that does one job perfectly beats a universal one that fails at ten. Add the second tool only after the first runs a week without a manual fix. I created a FREE course that teaches you to build an agent end to end with Claude Code. The course is taught inside Claude Code. The only thing you need is a $20 Claude subscription (Link in comments)

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

Last week, I shared how Gen AI is moving us from the age of information to the age of intelligence. Technology is changing rapidly and the way customers shop and buy is changing, too. We need to understand how the customer journey is evolving in order to drive customer connection today. That is our bread and butter at HubSpot - we’re deeply curious about customer behavior! So I want to share one important shift we’re seeing and what go-to-market teams can do to adapt. Traditionally, when a customer wants to learn more about your product or service, what have they done? They go to your website and explore. They click on different pages, filter for information that’s relevant to them, and sort through pages to find what they need. But today, even if your website is user-friendly and beautiful, all that clicking is becoming too much work. We now live in the era of ChatGPT, where customers can find exactly what they need without ever having to leave a simple chat box. Plus, they can use natural language to easily have a conversation. It's no surprise that 55% of businesses predict that by 2024, most people will turn to chatbots over search engines for answers (HubSpot Research). That’s why now, when customers land on your website, they don’t want to click, filter, and sort. They want to have an easy, 1:1, helpful conversation. That means as customers consider new products they are moving from clicks to conversations. So, what should you do? It's time to embrace bots. To get started, experiment with a marketing bot for your website. Train your bot on all of your website content and whitepapers so it can quickly answer questions about products, pricing, and case studies—specific to your customer's needs. At HubSpot, we introduced a Gen AI-powered chatbot to our website earlier this year and the results have been promising: 78% of chatters' questions have been fully answered by our bot, and these customers have higher satisfaction scores. Once you have your marketing bot in place, consider adding a support bot. The goal is to answer repetitive questions and connect customers with knowledge base content automatically. A bot will not only free up your support reps to focus on more complex problems, but it will delight your customers to get fast, personalized help. In the age of AI, customers don’t want to convert on your website, they want to converse with you. How has your GTM team experimented with chatbots? What are you learning? #ConversationalAI #HubSpot #HubSpotAI

If you are building AI agents or learning about them, then you should keep these best practices in mind 👇 Building agentic systems isn’t just about chaining prompts anymore, it’s about designing robust, interpretable, and production-grade systems that interact with tools, humans, and other agents in complex environments. Here are 10 essential design principles you need to know: ➡️ Modular Architectures Separate planning, reasoning, perception, and actuation. This makes your agents more interpretable and easier to debug. Think planner-executor separation in LangGraph or CogAgent-style designs. ➡️ Tool-Use APIs via MCP or Open Function Calling Adopt the Model Context Protocol (MCP) or OpenAI’s Function Calling to interface safely with external tools. These standard interfaces provide strong typing, parameter validation, and consistent execution behavior. ➡️ Long-Term & Working Memory Memory is non-optional for non-trivial agents. Use hybrid memory stacks, vector search tools like MemGPT or Marqo for retrieval, combined with structured memory systems like LlamaIndex agents for factual consistency. ➡️ Reflection & Self-Critique Loops Implement agent self-evaluation using ReAct, Reflexion, or emerging techniques like Voyager-style curriculum refinement. Reflection improves reasoning and helps correct hallucinated chains of thought. ➡️ Planning with Hierarchies Use hierarchical planning: a high-level planner for task decomposition and a low-level executor to interact with tools. This improves reusability and modularity, especially in multi-step or multi-modal workflows. ➡️ Multi-Agent Collaboration Use protocols like AutoGen, A2A, or ChatDev to support agent-to-agent negotiation, subtask allocation, and cooperative planning. This is foundational for open-ended workflows and enterprise-scale orchestration. ➡️ Simulation + Eval Harnesses Always test in simulation. Use benchmarks like ToolBench, SWE-agent, or AgentBoard to validate agent performance before production. This minimizes surprises and surfaces regressions early. ➡️ Safety & Alignment Layers Don’t ship agents without guardrails. Use tools like Llama Guard v4, Prompt Shield, and role-based access controls. Add structured rate-limiting to prevent overuse or sensitive tool invocation. ➡️ Cost-Aware Agent Execution Implement token budgeting, step count tracking, and execution metrics. Especially in multi-agent settings, costs can grow exponentially if unbounded. ➡️ Human-in-the-Loop Orchestration Always have an escalation path. Add override triggers, fallback LLMs, or route to human-in-the-loop for edge cases and critical decision points. This protects quality and trust. PS: If you are interested to learn more about AI Agents and MCP, join the hands-on workshop, I am hosting on 31st May: https://lnkd.in/dWyiN89z If you found this insightful, share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content.

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?

Your AI agent architecture is too thin. And in production, thin stacks don't bend. They break. I've built 400+ production AI agents. The ones that failed? Almost always missing the same layers. Here is the blueprint I wish I had on day one. 9 layers. In order. ✦ Layer 1: Input Validation -- Raw user text never touches the model directly. Never. We clean it. We enforce strict schemas. We classify intent before a single token hits the context window. PydanticAI forces the agent to return data in a strict JSON format. No schema, no entry. ✦ Layer 2: Context Engineering -- Most builders dump everything into the context window. All of it. Every message. Every document. Then wonder why the model loses focus. We compress. We compact. We keep the window lean, relevant, and surgical. 7 advanced methods exist for this. Most engineers know zero of them. ✦ Layer 3: Reasoning & Planning -- Prompt in. Response out. That's not an agent. That's autocomplete with a job title. Real agents think before they act. We use ReAct patterns. Recursive planning with LangChain. Directed acyclic graphs with LangGraph. The model doesn't guess the next step. It plans the whole path. ✦ Layer 4: Memory & State -- An agent that forgets the last conversation is not an agent. It's a stateless chatbot pretending to be one. We separate session memory from long-term knowledge. Persistent state stores. Continuity across every session. The agent remembers. Always. ✦ Layer 5: Tool & Action -- An agent without tools is just a storyteller. We use typed interfaces and MCP to give the agent real hands in the real world. And we build for failure. API timeouts. Rate limits. Retries. All handled at the execution layer. ✦ Layer 6: Orchestration -- Here is what happens when you skip this layer. Infinite loops. We use multi-agent routers. Specialized sub-agents. Directed control flow with LangGraph. The model doesn't figure out the steps. The graph defines them. ✦ Layer 7: Reflexion Engine -- We build a second process that reviews the agent's work before it ships. Checks alignment with the original goal. Flags before completion. One review loop prevents more failures than a hundred prompt rewrites. ✦ Layer 8: Observability & Eval -- You cannot fix what you cannot see. We track traces, token costs, and latency at every single step. Eval is not optional in production. It is the only feedback loop that matters. ✦ Layer 9: Governance & Safety -- PII redaction. Human-in-the-loop triggers for high-stakes decisions. Hard guardrails that stop non-compliant responses before they ever reach the user. If it breaks the rules, it never ships. Full stop. Most production agents are missing 6 of these 9 layers. 🗨️ Which layer is missing from yours? --- ⫸ꆛ Join 46,000+ engineers and get instant access: - 30-min Zero to Hero AI Agents Training + 50 Best Practices Guide (56 pages) + How to Build Production AI Agents Guide (32 pages) Grab it here: https://lnkd.in/eqDxeGFR

🤖 Should your chatbot look and act human-like? Most companies never think this through properly. They copy what others are doing, or go with what "feels right." 🎓 But research shows both approaches have specific use cases, and getting it wrong can backfire. 𝗛𝘂𝗺𝗮𝗻-𝗟𝗶𝗸𝗲 𝗰𝗵𝗮𝘁𝗯𝗼𝘁𝘀 (human-ish faces, empathetic language, informal tone): ✅ Best for: Delivering positive news like approvals or upgrades → People rated companies 8.1% higher when good decisions came from human-like bots ✅ Reduces likelihood of fraud → Human-like features increase guilt, lowering fraud attempts ❌ But they backfire with: Angry customers → Satisfaction dropped 23.4% when upset people dealt with human-like (vs machine-like) chatbots 𝗠𝗮𝗰𝗵𝗶𝗻𝗲-𝗟𝗶𝗸𝗲 𝗰𝗵𝗮𝘁𝗯𝗼𝘁𝘀 (robotic features, direct language, formal tone): ✅ Best for: Collecting sensitive information → People disclosed 11.5% more medical data to machine-like AI than to humans ✅ Embarrassing purchases → 11.2% higher engagement for sensitive products when the bot was clearly machine-like ❌ But they miss: Opportunities for recognition → Human-like bots are better at making people feel their individual merits are seen, for example when sharing good news 📈 Design your chatbot, and how it interacts, based on the situation in which it will be used. ⚡ Pro tip: if you have the technical capabilities, set rules that make the chatbot adapt in real-time. For example, if a customer is angry, the human-like chatbot can morph into using more direct language. 📘 If this topic interests you, my team and I created the Wharton Blueprint for Effective AI Chatbots in collaboration with the Wharton Human-AI Research department. 👉 It's free, and you can find it on Wharton's website with a quick online search.

Most people overcomplicating AI agents I’ve seen teams jump straight into frameworks and tooling before answering one basic question. What exactly should this agent do? Here’s a 10-step blueprint for building an AI agent that actually works. Whether you’re technical or non-technical, this applies. 1. Set the Objective Start with the problem, not the tech. Identify the core task, define what success looks like, and set clear boundaries. The best first agent? Automate the workflow you already do manually every single day. The boring, repetitive one. 2. Design the Core Instructions This is where most agents break. Give your agent a clear role, structured instructions, and guardrails. Think of it as writing a job description, not a prompt. If you gave these instructions to an employee, would they know exactly what to do? 3. Select the Right Model Not every task needs the most powerful model, think about context window limits, and always weigh cost against performance. Smart routing between models can cut costs by 60-70%. 4. Connect Tools & Systems An agent without tool access is just a chatbot. Integrate APIs, databases, CRMs, and automation workflows. Without tool integration, your agent stays informational instead of operational. The Model Context Protocol (MCP) is emerging as a key standard here. 5. Build Memory Capabilities Context is everything. Layer short-term conversation history, task-based working memory, and long-term storage using databases or vector stores. Agents without memory repeat mistakes 6. Add a Reasoning Layer This is what separates a basic chatbot from a real agent. This is where chain-of-thought and planning capabilities matter most. 7. Orchestrate the Workflow Define how everything connects. Managing how multiple agents communicate and maintain state is where the real complexity lives. 8. Design the User Experience A powerful agent with a bad interface is a wasted agent. 9. Test and Optimize Run functional and edge-case tests. Measure speed, accuracy, and reliability. Here’s the part most people skip: review your agent’s outputs the way you’d review a pull request. 10. Monitor and Scale This is where long-term success happens. Here’s why this matters right now: The agentic AI market is projected to hit roughly $10.8 billion in 2026, growing at over 40% annually. Gartner projects 40% of enterprise applications will include task-specific AI agents by the end of this year. And yet, only about a third of organizations have actually scaled their AI deployments beyond pilot programs. The gap between experimenting and executing is where the real opportunity. You don’t need to build the most sophisticated agent on day one. You need to build one that solves a real problem. What’s the first workflow you’d hand off to an AI agent? Follow Sufyan Maan, M.Eng. for more Join my newsletter: sufyannmaan.substack.com

We went from zero to 10,000 chatbot conversations per month in 90 days. No consultants. No six-month roadmap. Here's the exact process. Step 1: Define the scope (2 days). Pick one use case. We chose lead qualification. Document 10-15 common questions. Create qualification criteria. Step 2: Choose the platform (3 days). Evaluated 5 platforms. Picked Intercom. Criteria: Easy to build, CRM integration, under $500/month. The platform matters less than shipping fast. Step 3: Build conversation flows (5 days). Map the decision tree. We built 3 paths: Product demo request. Pricing inquiry. Technical support. Each path ends with booking or contact collection. Step 4: Write the copy (3 days). Write like a human. Short sentences. One question at a time. Casual tone beat professional by 23%. Step 5: Set up integrations (7 days). Connected to: CRM (HubSpot). Calendar (Calendly). Slack notifications. Longest step due to API limits. Step 6: Build knowledge base (4 days). Documented 25 FAQ responses. Pricing, features, timelines, support. Short, scannable answers only. Step 7: Test internally (5 days). 8 team members tested every path. Found and fixed: Typo handling issues. Dead-end conversation path. Calendar integration bugs. Step 8: Soft launch (7 days). Enabled for 10% of traffic. Monitored every conversation. Week 1 results: 47 conversations. 34% completion rate. 8% booking rate. Step 9: Iterate based on data (ongoing). Analyzed drop-offs. 62% abandoned after third question. Fix: Shortened from 7 questions to 4. New results: 58% completion rate. 19% booking rate. Step 10: Scale to 100%. After two weeks, enabled for all traffic. Month 1: 1,200 conversations. Month 2: 4,800 conversations. Month 3: 10,000 conversations. 23% of conversations book demos without human involvement. Total timeline: 90 days from start to 10K conversations. What we learned. Speed beats perfection. Ship in 30 days, iterate weekly. One use case done well beats ten done poorly. Watch drop-off points, fix them fast. Where are you in this process? Found this helpful? Follow Arturo Ferreira and repost ♻️