START BUILDING YOUR GENERATIVE AI SOLUTION

Constructing a compelling AI demo application that functions intermittently can be straightforward. However, to transition to a production stage, continuous iterations and enhancements to your LLM application’s performance are inevitable.

The three most common techniques for improving your GenAI application are

• • prompt engineering:  Creating specialized prompt to guide LLM behavior,
  • retrieval augmented generation (RAG): Combining an LLM with external data,
  • fine-tuning: Adapting a pre-trained LLM to specific datasets or domains

RETRIEVAL-AUGMENTED GENERATION

Retrieval-Augmented Generation (RAG) is an approach that enhances the capabilities of LLMs for specific domains or an organization’s internal knowledge base, without requiring model retraining. 

DATA PREPARATION AND RAG IMPROVEMENTS

• Source Data Cleaning: Date Preprocessing, Metadata Extraction;
• Data Chunking/Splitting
• Embedding Models
• Creating Vector DB and retrieval strategy
• LLM: performance, fine tuning