– Gather diverse data sources (text, images, audio, video)

– Preprocess data (cleaning, deduplication, PII handling)

Data Cleaning: Clean and preprocess the data to remove noise, inconsistencies, and irrelevant information.

Data Chunking: Break down large documents into smaller, manageable chunks. This can be done based on semantic meaning, paragraph boundaries, or fixed-size chunks.

– Implement multimodal chunking strategies

– Select or fine-tune embedding models for different modalities

– Generate embeddings for all data types

– Experiment with domain-specific embedding models

– Choose a scalable vector database (e.g., Pinecone, Weaviate, Quadrant , MongoDB, Elasticsearch)

– Index embeddings with metadata

– Implement hybrid search capabilities (dense and sparse retrieval)

– Develop query understanding and intent classification

– Implement query expansion and reformulation techniques

– Create multimodal query handling (text, image, voice inputs)

– Implement dense retrieval with customizable parameters

– Develop re-ranking algorithms for improved relevance

– Create ensemble retrieval methods combining multiple strategies

Search Mechanism: Implement hybrid search methods (dense + sparse retrieval) for optimal results

– Design dynamic prompt engineering techniques

– Implement iterative retrieval for complex queries

– Develop context fusion methods for multimodal data

– Select and integrate appropriate LLMs ( OpenAI GPT-4 Anthropic Claude

Google PaLM ,Mistral AI ,Open-source models (LLaMA, Falcon)) for various use cases

– Implement model switching based on query complexity

– Develop fine-tuning pipelines for domain-specific tasks

– Implement multi-step reasoning for complex queries

– Develop fact-checking and hallucination detection mechanisms

– Create response formatting for different output modalities

– Implement comprehensive evaluation metrics (relevance, coherence, factuality)

– Develop feedback loops for continuous improvement

– Optimize system performance and latency

– Design a modular, microservices-based architecture

– Implement caching and load balancing strategies

– Develop monitoring and logging systems for production environments

– Create intuitive interfaces for various use cases (chatbots, search engines, recommendation systems)

– Develop APIs for easy integration with existing systems

– Implement user feedback mechanisms for system improvement

– Implement data encryption and access control measures

– Ensure compliance with relevant regulations (GDPR, CCPA)

– Develop audit trails for data usage and model decisions