The article explains six essential strategies for customizing Large Language Models (LLMs) to better meet specific business needs or domain requirements. These strategies include Prompt Engineering, Decoding and Sampling Strategy, Retrieval Augmented Generation (RAG), Agent, Fine-Tuning, and Reinforcement Learning from Human Feedback (RLHF). Each strategy is described with its benefits, limitations, and implementation approaches to align LLMs with specific objectives.
A list of 13 open-source software for building and managing production-ready AI applications. The tools cover various aspects of AI development, including LLM tool integration, vector databases, RAG pipelines, model training and deployment, LLM routing, data pipelines, AI agent monitoring, LLM observability, and AI app development.
- Composio - Seamless integration of tools with LLMs.
- Weaviate - AI-native vector database for AI apps.
- Haystack - Framework for building efficient RAG pipelines.
- LitGPT - Pretrain, fine-tune, and deploy models at scale.
- DsPy - Framework for programming LLMs.
- Portkey's Gateway - Reliably route to 200+ LLMs with one API.
- AirByte - Reliable and extensible open-source data pipeline.
- AgentOps - Agents observability and monitoring.
- ArizeAI's Phoenix - LLM observability and evaluation.
- vLLM - Easy, fast, and cheap LLM serving for everyone.
- Vercel AI SDK - Easily build AI-powered products.
- LangGraph - Build language agents as graphs.
- Taipy - Build AI apps in Python.
- Proxy fine-tuning is a method to improve large pre-trained language models without directly accessing their weights.
- It operates on top of black-box LLMs by utilizing only their predictions.
- The approach combines elements of retrieval-based techniques, fine-tuning, and domain-specific adaptations.
- Proxy fine-tuning can be used to achieve the performance of heavily-tuned large models by only tuning smaller models.
