Key Challenges In NLP For Chatbot Development

AI models like ChatGPT and Claude are powerful, but they aren’t perfect. They can sometimes produce inaccurate, biased, or misleading answers due to issues related to data quality, training methods, prompt handling, context management, and system deployment. These problems arise from the complex interaction between model design, user input, and infrastructure. Here are the main factors that explain why incorrect outputs occur: 1. Model Training Limitations AI relies on the data it is trained on. Gaps, outdated information, or insufficient coverage of niche topics lead to shallow reasoning, overfitting to common patterns, and poor handling of rare scenarios. 2. Bias & Hallucination Issues Models can reflect social biases or create “hallucinations,” which are confident but false details. This leads to made-up facts, skewed statistics, or misleading narratives. 3. External Integration & Tooling Issues When AI connects to APIs, tools, or data pipelines, miscommunication, outdated integrations, or parsing errors can result in incorrect outputs or failed workflows. 4. Prompt Engineering Mistakes Ambiguous, vague, or overloaded prompts confuse the model. Without clear, refined instructions, outputs may drift off-task or omit key details. 5. Context Window Constraints AI has a limited memory span. Long inputs can cause it to forget earlier details, compress context poorly, or misinterpret references, resulting in incomplete responses. 6. Lack of Domain Adaptation General-purpose models struggle in specialized fields. Without fine-tuning, they provide generic insights, misuse terminology, or overlook expert-level knowledge. 7. Infrastructure & Deployment Challenges Performance relies on reliable infrastructure. Problems with GPU allocation, latency, scaling, or compliance can lower accuracy and system stability. Wrong outputs don’t mean AI is "broken." They show the challenge of balancing data quality, engineering, context management, and infrastructure. Tackling these issues makes AI systems stronger, more dependable, and ready for businesses. #LLM

We've spoken with 30 companies who developed RAG-based chatbots on PDF documents. Every single one has failed: Core issues: 1) In vector space, "non-dairy products" is often closer to "milk" than "meat," this is a fundamental flaw of vector embedding search because they're very lossy. 2) Splitting documents into smaller chunks disrupts coherence, breaking cross-references and context. 3) Adopting new RAG architectures, re-embedding chunks with new models, and rerankers requires continuous, costly data (re)engineering efforts. 4) No Support for Aggregations – Vector search struggles with queries requiring aggregation (e.g., max, min, total), making it unreliable for analytical use cases. As a result, companies band-aid their chatbots by writing complex heuristics to patch these failures. Ironically, many end up going back to rule-based chatbots. Our advice is simple - Do You Even Need RAG? LLM models are dirt cheap now and quite comparable to embedding models. If your documents are small: just load them directly into the LLM context. If your documents are large: Enrich with rich metadata and query the right documents and pages based on the metadata. Chatting on documents must be redesigned.

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? 🚀

Many companies have started experimenting with simple RAG systems, probably as their first use case, to test the effectiveness of generative AI in extracting knowledge from unstructured data like PDFs, text files, and PowerPoint files. If you've used basic RAG architectures with tools like LlamaIndex or LangChain, you might have already encountered three key problems: 𝟭. 𝗜𝗻𝗮𝗱𝗲𝗾𝘂𝗮𝘁𝗲 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: Existing metrics fail to catch subtle errors like unsupported claims or hallucinations, making it hard to accurately assess and enhance system performance. 𝟮. 𝗗𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁𝘆 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: Standard RAG methods often struggle to find and combine information from multiple sources effectively, leading to slower responses and less relevant results. 𝟯. 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘁𝗼 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗮𝗻𝗱 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀: Basic RAG approaches often miss the deeper relationships between information pieces, resulting in incomplete or inaccurate answers that don't fully meet user needs. In this post I will introduce three useful papers to address these gaps: 𝟭. 𝗥𝗔𝗚𝗖𝗵𝗲𝗸𝗲𝗿: introduces a new framework for evaluating RAG systems with a focus on fine-grained, claim-level metrics. It proposes a comprehensive set of metrics: claim-level precision, recall, and F1 score to measure the correctness and completeness of responses; claim recall and context precision to evaluate the effectiveness of the retriever; and faithfulness, noise sensitivity, hallucination rate, self-knowledge reliance, and context utilization to diagnose the generator's performance. Consider using these metrics to help identify errors, enhance accuracy, and reduce hallucinations in generated outputs. 𝟮. 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗥𝗔𝗚: It uses a labeler and filter mechanism to identify and retain only the most relevant parts of retrieved information, reducing the need for repeated large language model calls. This iterative approach refines search queries efficiently, lowering latency and costs while maintaining high accuracy for complex, multi-hop questions. 𝟯. 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚: By leveraging structured data from knowledge graphs, GraphRAG methods enhance the retrieval process, capturing complex relationships and dependencies between entities that traditional text-based retrieval methods often miss. This approach enables the generation of more precise and context-aware content, making it particularly valuable for applications in domains that require a deep understanding of interconnected data, such as scientific research, legal documentation, and complex question answering. For example, in tasks such as query-focused summarization, GraphRAG demonstrates substantial gains by effectively leveraging graph structures to capture local and global relationships within documents. It's encouraging to see how quickly gaps are identified and improvements are made in the GenAI world.