Blog & Insights
Engineering insights, best practices, and technology guides from the SIMA DigiTech team.
RAG Architecture: From Document Ingestion to Retrieval
A complete walkthrough of retrieval-augmented generation: chunking, embeddings, retrieval, and evaluation.
Document Chunking Strategies for RAG
Fixed-size, semantic, and hierarchical chunking — how chunking quality drives RAG answer accuracy.
How Vector Search Works in AI Applications
Embeddings, similarity metrics, and approximate nearest neighbor search explained step by step.
Integrating LLMs into Existing Business Applications
Prompt design, function calling, guardrails, and cost control for production LLM integrations.
AI Agents vs. Traditional Automation
How AI agents differ from deterministic automation and when each approach makes sense.
Prompt Injection and LLM Guardrails
The security risks of LLM-powered systems and the guardrails that reduce them.
Evaluating RAG Applications Beyond Vibes
Retrieval metrics, relevance judgment, and evaluation harnesses for measuring RAG quality.
Reducing LLM Hallucinations: Techniques That Help
Grounding, retrieval, temperature, and self-consistency — practical techniques to reduce hallucinations.
Choosing a Vector Database: A Practical Guide
Dedicated vector stores vs. extensions, and the criteria for picking the right one for your workload.
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