Measure RAG Retrieval: MRR, NDCG, Hit Rate Explained
Retrieval-Augmented Generation RAG systems don't magically know the answer; they retrieve relevant documents first and then generate.
RAG Articles
50 articles
Secure Your RAG Pipeline Against Prompt Injection
Prompt injection is the silent killer of RAG security, where an attacker subtly manipulates your RAG system's behavior by embedding malicious instructio.
Self-RAG: Ground Answers Through Iterative Reflection
Self-RAG is a technique that allows Large Language Models LLMs to critically evaluate their own generated text and retrieve relevant information to impr.
RAG Sentence Window Retrieval: Expand Context Smartly
RAG Sentence Window Retrieval works by expanding the context around a retrieved document chunk to include surrounding sentences, ensuring the LLM has a .
Step-Back Prompting in RAG: Abstract Before Retrieving
Step-back prompting in RAG is actually about avoiding the initial retrieval, not improving it. Let's see what this looks like in practice
RAG Table and Image Extraction: Parse Non-Text Content
The RAG system can't parse non-text content because its core logic is designed for string manipulation, and it's encountering binary data or structured .
RAG with Tool Use: Integrate Agents for Dynamic Retrieval
RAG with Tool Use: Integrate Agents for Dynamic Retrieval The most surprising thing about RAG with tool use is that the "retrieval" part often becomes t.
Compare Vector Databases for RAG: Pinecone, Weaviate, Qdrant
Vector databases are the engine behind Retrieval Augmented Generation RAG, but choosing the right one for your needs can feel like picking a needle in a.
RAG A/B Testing: Compare and Validate Retrieval Strategies
RAG A/B testing is less about comparing two AIs and more about comparing how well two different retrieval mechanisms can feed information to a single AI.
Agentic RAG: Build Multi-Step Planning Pipelines
Agentic RAG transforms simple retrieval into a dynamic, multi-step reasoning process. Imagine you have a complex question that requires more than just f.
RAG Architecture: Every Component Explained
Retrieval Augmented Generation RAG isn't just about finding relevant documents; it's a sophisticated dance where a language model learns to ask better q.
RAG Chunking: Find the Optimal Chunk Size
The most surprising thing about RAG chunking is that bigger chunks aren't always better, and sometimes, much smaller chunks can lead to dramatically imp.
RAG Citations: Ground Every Answer with Source Attribution
A RAG system's most surprising output isn't the answer itself, but the precise, verifiable lineage of that answer back to its source documents.
Build a Code Repository RAG Pipeline
A code repository is a latent knowledge base, and RAG is the key to unlocking its secrets without needing to train a massive, proprietary model.
ColPali RAG: Multimodal Document Retrieval with Visuals
ColPali RAG: Multimodal Document Retrieval with Visuals — practical guide covering rag setup, configuration, and troubleshooting with real-world examples.
RAG Contextual Compression: Filter Irrelevant Passages
RAG Contextual Compression: Filter Irrelevant Passages — practical guide covering rag setup, configuration, and troubleshooting with real-world examples.
Anthropic Contextual Retrieval: Boost RAG Accuracy
The core problem Anthropic's contextual retrieval solves isn't just finding relevant documents, but actively shaping the LLM's understanding by filterin.
RAG with Conversation History: Build Multi-Turn QA
RAG with Conversation History: Build Multi-Turn QA — practical guide covering rag setup, configuration, and troubleshooting with real-world examples.
Corrective RAG: Adapt Retrieval When Confidence Is Low
The most surprising thing about RAG is that retrieval, the very foundation of RAG, is often its weakest link, and we've been largely ignoring it.
Reduce RAG Costs: Caching, Batching, Model Selection
Caching, batching, and model selection aren't just optimizations; they're fundamental to making Retrieval Augmented Generation RAG economically viable f.
Fine-Tune Embeddings for Domain-Specific RAG
Fine-tuning embeddings for your Retrieval Augmented Generation RAG system can dramatically improve its ability to understand and retrieve information re.
RAG Embedding Cache: Cut Latency and API Costs
The most surprising thing about RAG embedding caches is that they don't actually store embeddings; they store the queries that produced those embeddings.
Choose the Right Embedding Model for Your RAG Pipeline
Embedding models are the unsung heroes of Retrieval Augmented Generation RAG, and picking the wrong one can turn your sophisticated pipeline into a glor.
Build a Production RAG Pipeline End to End
Retrieval Augmented Generation RAG pipelines are often described as "just connecting a retriever to a generator," but the real magic, and the most surpr.
RAG Enterprise Architecture: Scale to Millions of Docs
A RAG system's true power isn't in its retrieval accuracy, but in how it uses that retrieved information to generate a coherent and contextually relevan.
Evaluate RAG with RAGAS: Faithfulness, Recall, Precision
The most surprising thing about evaluating Retrieval Augmented Generation RAG is that the metrics you think are about generation quality are actually re.
GraphRAG: Combine Knowledge Graphs with Vector Search
GraphRAG isn't just about stuffing your knowledge graph into a vector database; it's about getting vector search to understand the relationships in your.
Reduce RAG Hallucinations: Grounding and Verification
The most surprising thing about reducing RAG hallucinations is that the problem isn't just about finding more relevant documents, but about how the retr.
RAG Hybrid Search: Combine BM25 and Semantic Retrieval
RAG Hybrid Search: Combine BM25 and Semantic Retrieval — practical guide covering rag setup, configuration, and troubleshooting with real-world examples.
HyDE RAG: Generate Hypothetical Documents to Improve Recall
HyDE is a technique that uses a large language model LLM to generate a hypothetical answer to a user's query, and then uses that hypothetical answer as .
Optimize RAG Indexing: Faster Ingestion at Scale
The core innovation of RAG isn't just retrieving documents; it's retrieving relevant snippets based on a query, and the indexing process is where that s.
RAG Ingestion: Batch and Incremental Update Strategies
RAG ingestion isn't just about loading data; it's about intelligently managing its lifecycle to keep your retrieval system sharp and responsive.
Keep Your RAG Knowledge Base Fresh: Update Strategies
The most surprising truth about keeping a Retrieval Augmented Generation RAG knowledge base fresh is that the "freshness" problem isn't about how often .
Late Chunking in RAG: Preserve Context Across Chunks
Late chunking fundamentally breaks the continuity of information by splitting documents at arbitrary points, making it impossible for a RAG system to re.
Optimize RAG Latency: Hit P99 Targets in Production
Retrieval Augmented Generation RAG often feels like a black box where latency just happens, but the real secret is that most of the P99 tail is usually .
RAG LLM Cache: Semantic Deduplication for Speed
Retrieval Augmented Generation RAG LLM caches are often described as simply storing past queries and their results, but their real power, and a signific.
Long Context vs RAG: When Each Approach Wins
Long context windows are surprisingly often worse than RAG for tasks requiring factual recall. Imagine you're trying to answer a question about a specif.
RAG Metadata Filtering: Query Structured Data Precisely
RAG metadata filtering lets you go beyond simple keyword matching to retrieve documents based on precise, structured data attributes, dramatically impro.
Monitor RAG Retrieval Quality in Production
Retrieval Augmented Generation RAG systems, when deployed in production, face a unique challenge: the quality of the retrieved context directly dictates.
RAG Multi-Query: Generate Query Variants with an LLM
RAG Multi-Query doesn't just generate more questions; it fundamentally changes how retrieval works by treating search as a language problem, not a keywo.
RAG Multi-Tenant: Isolate Data Between Customers
A multi-tenant RAG system can actually provide stronger data isolation than a single-tenant setup, if designed correctly.
Multimodal RAG: Retrieve Across Images and Text
Retrieval augmented generation RAG typically treats text and images as completely separate entities, but what if they could talk to each other.
RAG Open Source vs Managed: Compare Costs and Trade-offs
Open-source RAG solutions can be cheaper than managed services, but the total cost of ownership TCO often favors managed services due to hidden operatio.
RAG Parent-Child Retrieval: Expand Context on Demand
Retrieval Augmented Generation RAG often struggles with retrieving only the most relevant snippets, leading to either too much noisy context or too litt.
RAG PDF Ingestion: Parse Tables, Images, Complex Layouts
PDFs are often treated as opaque blobs, but the real magic is how a RAG system can coax structured data out of them, even when the layout is a mess.
RAG Production Pipeline: Reliable Architecture Patterns
The most surprising thing about RAG production pipelines is that their reliability often hinges on what you don't retrieve, not just what you do.
RAG Query Routing: Direct Queries to the Right Index
RAG query routing is all about ensuring that when a user asks a question, the right piece of information is retrieved from your knowledge base, and not .
RAG Query Transformation: Rewrite Queries for Better Recall
The most surprising thing about query rewriting for RAG is that the LLM often makes your search worse if you don't guide it precisely.
RAG Fusion: Merge Rankings with Reciprocal Rank Fusion
RAG Fusion is a technique that combines multiple search results to produce a single, more relevant ranking, often outperforming individual search method.
RAG Reranking: Cohere and Cross-Encoders for Precision
Reranking with Cohere and cross-encoders is surprisingly effective because it shifts the focus from retrieving any relevant document to retrieving the m.