Large Language Models (LLMs) comes with built in knowledge and capable of Content Manipulation (creation, summarization, paraphrasing, translation, etc), Question Answering, Inferring Conclusions, Learning Patterns, etc. Prior to LLMs, all above tasks were only possible by intelligent humans. But LLMs have made it possible for super fast computers to carry out these actions with high accuracy.
Retrieval Augmented Generation
Retrieval Augmented Generation
Retrieval Augmented Generation [RAG] is a way to feed context-specific, accurate and timely external information to LLMs. When combine these two aspects (1. LLM capabilities and 1. knowledge specific to a domain/entity), many entities could develop solutions to cater their internal and external information needs at much efficient way compared to getting humans involved.
RAG Workflow
RAG Workflow
A diagram illustrating generic workflow of a RAG system
Why Businesses Need RAG Solutions Today
Why Businesses Need RAG Solutions Today
RAG-powered solutions can be used for
These solutions reduce operational workload, increase accuracy, and improve the speed of information access
RAG related models & tools
RAG related models & tools
LLM & RAG Options
LLM & RAG Options
RAG Fine tuning
RAG Fine tuning
Probabilistic vs Deterministic
Probabilistic vs Deterministic
Software systems are usually deterministic, meaning, for a given set of inputs, they always produce same set of outputs. This is what we are used to but when we use LLMs, certain parts of the solution become probabilistic. Meaning, it will not always produce the same set of outputs for a given set of inputs. This introduces a great number of difficulties when developing solutions using LLMs.
This uncertainty even could lead to unprecedented project failures. A PoC and an MVP which gave promising results could miserably fail in a production setup. So it is important to understand this complexity and deal with it in a pragmatic way from the initial stage of the solution development. And this found to be a common pitfall for many RAG solutions.
