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When a RAG assistant gives a wrong answer, the first reaction is usually to blame the language model. Teams try a bigger model, rewrite the prompt, or add more instructions. Often none of it helps, because the model never saw the right information. A model cannot use a passage it was never given, and it will confidently answer from whatever it was given instead. This blog explains why retrieval should be evaluated first, how to separate retrieval failures from generation fail

A RAG system lives or dies on what it retrieves. If the right passage never reaches the language model, the answer will be wrong, however good the model is. Re-ranking is a popular fix. The system retrieves a larger set of candidate passages, then a second model reads each one alongside the question and puts the best at the top. It often improves relevance. It also adds time to every request, and that time is paid by every user. This blog explains what re-ranking does, what i

A RAG bot that gets only about three answers in ten right is not rare. It is the normal result when a team connects a language model to a pile of documents, sees a few good demos, and ships. The assistant sounds fluent, so the wrong answers are easy to miss until a customer, an auditor, or an employee acts on one. The good news is that low accuracy in retrieval-augmented generation is usually a pipeline problem, not a model problem, and pipeline problems can be measured and f

Most enterprise AI assistants answer questions by finding the passages that look most similar to the question and handing them to a language model. That works well for lookups, but it struggles when the answer depends on how things are connected, such as which systems fail when one service goes down, or what the main themes are across thousands of documents. GraphRAG addresses this by adding a knowledge graph to retrieval, so the system can follow relationships and reason acr

An AI system that reads loan documents, summarizes customer emails, or drafts responses to account queries can save a bank thousands of hours, but only if it can be trusted with sensitive data and cannot be manipulated into doing something it should not. A secure AI banking operations workflow is the set of controls, checkpoints, and monitoring that makes that trust possible, and for banking leaders it is less about the model itself and more about everything built around it.

Picture two lawyers using AI for the same research task. The first receives a confident, well-formatted answer with five case citations and files it without checking. Two of the cases do not exist, and the lawyer ends up explaining the mistake to a judge. The second uses an assistant that finds the relevant cases in minutes, shows the exact passage behind every claim, and flags the one point it could not support. A task that once took hours takes a fraction of the time, and e

Imagine a shopper who buys a washing machine online for S$2,000 and receives a broken one. The seller refuses a refund, so the shopper takes the dispute to a small claims tribunal. Lawyers are not allowed in that room. The evidence is a pile of WhatsApp messages and emails. The forms are confusing, and the documents may not even be in the shopper's first language. For decades, people in this position were largely on their own. Today, courts in several countries are using arti

In this guide, you’ll create a fully functional local RAG pipeline in Python that can: Read custom documents Convert them into embeddings Store them in a vector database Retrieve relevant context Generate grounded answers using an LLM By the end, you’ll have a complete command-line RAG application running locally on your machine.

Semantic chunking is a chunking strategy that groups text based on meaning rather than fixed size. Instead of splitting text after a certain number of tokens, semantic chunking tries to identify: topic boundaries, semantic transitions, and coherent conceptual units. The goal is simple: Keep semantically related information together.

Sliding window chunking has become one of the most widely used retrieval strategies because it helps preserve continuity between chunks without requiring complex semantic analysis.

Chunking is the process of splitting documents into smaller retrievable units before embedding and indexing them. In a RAG pipeline: Documents are split into chunks. Each chunk is converted into embeddings. The embeddings are stored in a vector database. User queries retrieve the most relevant chunks. The retrieved chunks are passed to the LLM as context. This means retrieval quality depends heavily on chunk quality.

At a high level, Vectorless RAG is exactly what the name suggests: A Retrieval-Augmented Generation system that avoids using vector embeddings and vector databases for retrieval.

In this guide, we’ll break down the major internal components of a RAG pipeline step by step in plain English. We’ll cover: chunking, embeddings, vector databases, similarity search, retrieval, and context injection into LLMs.

A fine-tuned model can become much better at understanding domain-specific language, following certain workflows, or generating responses in a particular style. Traditional fine-tuning approaches tried to push knowledge into the model. RAG flips the approach completely. Instead of permanently storing information inside model weights, RAG allows the AI to retrieve relevant information dynamically at runtime.

At a high level, RAG is basically a technique that helps AI look up information before answering you instead of relying only on what it remembers from training.

A complete technical guide to building an AI PDF Chatbot using LangChain, FAISS, and OpenAI. Covers RAG architecture, chunking strategy, vector store setup, system prompt design, and deployment — without the source code guesswork.

Vector Database Job Support & Interview Preparation — Get the Role You Are Targeting Vector database skills are now tested in interviews at Google, Amazon, Flipkart, Swiggy, PhonePe, Meesho, CRED, and every AI-first startup. But most preparation resources stop at theory — they do not prepare you for the system design rounds, coding challenges, and production-depth questions that top companies actually ask. At Codersarts, our experts prepare you with the exact content that get

Add AI Search to Your Existing App — Without Rebuilding Everything Your users expect a search that actually understands what they are looking for. Adding semantic AI search to an existing product does not require a complete rebuild — it requires the right integration strategy. We add vector search to live applications without disrupting your current codebase, database, or user experience. How We Integrate AI Search into Your App ✓ Audit your existing data and search requirem

Vector Database Implementation Help — Expert Developers, Production-Ready Code Implementing a vector database is not like adding a REST API endpoint. It requires the right embedding model, indexing strategy, query architecture, and integration layer — and getting any one of them wrong costs weeks of debugging and re-work. At Codersarts, our vector database engineers have built production pipelines across every major platform — Pinecone, Weaviate, Qdrant, Milvus, ChromaDB, pgv

RAG Project Help for Students — Build AI That Reads Your Documents Retrieval-Augmented Generation (RAG) is the most in-demand AI project topic of 2025. Almost every university AI, NLP, and data science course now includes a RAG-based project — and it is one of the most technically challenging to implement from scratch. Our experts help you design, build, and understand a complete RAG pipeline so you can demonstrate it confidently in your viva, presentation, or project report.