RAG & Data
Guides to retrieval-augmented generation, vector and hybrid search, data preparation, evaluation, and secure retrieval.
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Memory Architectures for AI Agents: Short-Term, Long-Term, Episodic and Semantic Memory
A practical way to separate working context, durable facts, past experiences, and business knowledge in an agent memory design.
2 min read -
pgvector for RAG: HNSW, IVFFlat, Filtering and Tenant Isolation
A practical guide to using pgvector in PostgreSQL for RAG, including index choice, metadata filters, recall, and tenant boundaries.
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Hybrid Search in PostgreSQL: Combining Keyword and Vector Retrieval
Why good RAG often combines lexical search, vector similarity, metadata filters, and reranking instead of trusting one retrieval method.
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Prompt Engineering vs Context Engineering for AI Agents
Prompt wording matters, but agent quality usually improves faster when the surrounding context, tools, permissions, and evidence are designed well.
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Building Agent Memory with PostgreSQL: JSONB, pgvector and the Source of Truth
A practical PostgreSQL design for agent memory that combines structured facts, embeddings, metadata filters, expiry, and clear ownership.
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AI Agents for Customer-Service Knowledge Search
A practical guide to building an internal customer-service knowledge agent with governed sources, permission-aware retrieval, citations, feedback, and human escalation.
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Context Engineering for AI Agents: Give the Right Information
Context engineering is the everyday discipline of giving an agent the right background for one task without burying it in irrelevant or unsafe text.
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RAG vs Fine-Tuning for Business Knowledge: Which Should You Use?
RAG keeps changing knowledge current, while fine-tuning changes repeatable behaviour. The right choice depends on which problem is costing you time.
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AI Agent Memory: What to Store, Retrieve and Forget
Agent memory should save people effort without turning guesses, stale facts, and private conversations into permanent truth.
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How to Prepare Business Data for an AI Agent
A practical data-readiness guide covering source ownership, structure, permissions, freshness, retrieval tests, and knowledge-base maintenance.
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RAG Chatbot vs AI Agent: Which Does a Business Need?
A practical comparison of RAG chatbots and AI agents, including when to use each, when to combine them, and how to choose a safe first workflow.
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RAG Evaluation Datasets for Business Questions
A RAG evaluation dataset is a curated set of questions, expected evidence, acceptable answers, and known failure cases. It gives a team something stable to test when sources,…
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What Is RAG? A Practical Guide for Modern AI Applications
Retrieval-Augmented Generation (RAG): The Future of AI-Driven Business Communication In today’s digital-first world, the pressure on businesses to deliver instant, intelligent, and reliable information is greater than ever.…
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RAG vs. Traditional Chatbots: Which is Right for Your Business?
In today’s digital economy, AI chatbots are no longer a futuristic novelty—they’re a strategic necessity. But not all chatbots are created equal. Businesses now face a critical decision:…
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Memory Management in Large-Scale RAG Deployments
How to size and reduce memory in RAG systems: estimate vectors and indexes, choose compression carefully, separate database from model memory, and test retrieval quality.
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Caching Strategies for High-Performance RAG Systems
A practical guide to caching RAG embeddings, retrieval results and answers without serving stale data or crossing tenant boundaries. Includes key design, invalidation, evaluation and rollout advice.
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Quantum Computing & RAG: What’s Practical and What’s Speculative?
Quantum algorithms are not a drop-in RAG speed upgrade. Learn what Grover, HHL and post-quantum cryptography do—and what to benchmark before investing.
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RAG Chunking Strategies: Fixed, Structure-Aware & Semantic
Compare fixed-size, structure-aware, semantic and hierarchical chunking. Learn how to preserve context, measure retrieval quality and choose the simplest approach that works.
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Fine-Tuning vs RAG: When to Adapt the Model and When to Retrieve
Understand when RAG, fine-tuning or both fit. Compare strengths, limits, data risks and a practical evaluation workflow—without assuming tuning makes answers accurate.
6 min read -
Optimizing Vector Search Performance for Large Document Collections
Introduction As enterprises adopt Retrieval-Augmented Generation (RAG) to build reliable AI assistants, one of the biggest challenges is performance at scale. It’s relatively easy to build a prototype…
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Understanding Vector Databases in RAG Systems
Introduction As organizations embrace Retrieval-Augmented Generation (RAG) to build smarter chatbots, digital assistants, and enterprise search platforms, one technology has quietly become the backbone of this new ecosystem:…
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Hybrid RAG: Combine Keyword and Vector Search Effectively
Learn when hybrid RAG helps, how keyword and vector retrieval complement each other, how to fuse rankings safely, and how to evaluate quality, permissions, latency and cost.
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Contextual RAG: Conversation Context, Memory & Retrieval
Contextual retrieval is what lets a RAG assistant understand a follow-up such as “Does that apply to the diesel model too?” without treating it as a brand-new question.…
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Real-Time RAG: Handling Live Data in Retrieval-Augmented Systems
In fast-paced industries—finance, e‑commerce, news, and IoT monitoring—stale information can lead to bad decisions, missed opportunities, or customer frustration. Real‑Time Retrieval‑Augmented Generation (RAG) addresses this challenge by combining…
7 min read