Decoding Data Science retweeted
BEYOND THE PROMPT xitter.cf/i/broadcasts/1NGarowok…
Decoding Data Science retweeted
A RAG chatbot is only as good as its indexing pipeline.
Before an LLM can answer questions from enterprise policies, the data must be prepared correctly:
• Split documents into meaningful chunks
• Add overlap to preserve context
• Convert chunks into embeddings
• Store them in a vector database
• Use the same embedding model for documents and queries
Good retrieval starts long before the prompt.
If your chatbot gives incomplete or irrelevant answers, check the indexing strategy before blaming the model.
What has improved your RAG accuracy the most: chunking, embeddings, retrieval, or reranking?
Decoding Data Science retweeted
Most AI agents work well in demos. Production is where the real engineering begins.
A reliable Agentic AI system requires six core building blocks:
Intelligent model routing
Strict tool contracts
State and memory separation
Deterministic orchestration
Trace-level evaluation
Human approval for high-impact actions
The goal is not maximum autonomy. It is controlled, observable and reliable execution.
Here is a practical blueprint for moving AI agents from demos to production.
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