Tag

#RAG

5 articles tagged #RAG.

AI Integration/LLM Engineering·May 21, 2026·8 min read

LLM Hallucination in Production: Mitigation Strategies That Actually Work

Hallucination is not a bug that gets patched in the next model release. It is a property of how language models work. Here are the patterns that actually reduce it in production systems, and what they cost.

AILLMProduction

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RAG in 2026: Beyond Naive Vector Search to Production Architectures

P.02

AI Integration/Engineering·Feb 28, 2026·14 min read

RAG in 2026: Beyond Naive Vector Search to Production Architectures

A systematic comparison of modern RAG approaches in 2026: ColBERT, SPLADE, hybrid search, contextual retrieval, and late interaction models. Benchmarks, architecture tradeoffs, and when RAG beats fine-tuning.

RAGVector SearchLLM

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Fine-Tuning vs Prompting vs RAG — A Decision Framework That Actually Works

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AI Integration/Guide·Feb 21, 2026·21 min read

Fine-Tuning vs Prompting vs RAG — A Decision Framework That Actually Works

Stop guessing which AI approach to use. This decision framework with real cost, latency, and accuracy comparisons helps you pick the right one every time.

AIFine-TuningRAG

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RAG Is Dead, Long Live RAG — What Contextual Retrieval Actually Looks Like in 2026

P.04EditorPick

AI Integration/Engineering·Feb 21, 2026·18 min read

RAG Is Dead, Long Live RAG — What Contextual Retrieval Actually Looks Like in 2026

Naive RAG is broken. Here is how contextual retrieval, hybrid search, and intelligent chunking are reshaping how we build AI applications in 2026.

AIRAGVector Search

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RAG vs Fine-Tuning vs Prompt Engineering: Which AI Strategy Fits Your Product?

P.05EditorPick

AI Integration/Guide·Jan 28, 2026·22 min read

RAG vs Fine-Tuning vs Prompt Engineering: Which AI Strategy Fits Your Product?

Three approaches to customizing AI for your use case, with cost comparisons, performance benchmarks, implementation timelines, and a decision framework. The guide we wish existed when we started.

RAGFine-TuningPrompt Engineering

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