We are entering the era of small & highly efficient models!

Prompt engineering, or prompt design, is crafting instructions for LLMs to get desired responses. It’s essential for ensuring accurate, high-quality responses from a large language model.

Implementing RAG presents a challenge, especially when it comes to effectively parsing and understanding tables in unstructured documents. This is particularly difficult with scanned documents or documents in image format. There are at least three aspects of these challenges:

Generative AI (GenAI) enables advanced AI use cases and innovation but also changes how the enterprise architecture looks like. Large Language Models (LLM), Vector Databases, and Retrieval Augmentation Generation (RAG) require new data integration patterns and data engineering best practices.

If you’re building any meaningful product/feature with LLMs (large language models), you’ll probably use the technique called RAG (retrieval-augmented generation). It can allow you to integrate external data that was not available in the LLM’s training data into the LLM’s text generation process, which can greatly reduce the nightmare of hallucination and improve the relevance of the text responses.