Here are some advanced RAG techniques
Input/output validation: Ensuring groundedness. This technique verifies that the input query and generated output align with specific use cases and company policies. It helps maintain control over the LLM’s responses, preventing unintended or harmful outputs.
Guardrails: Compliance and auditability. Guardrails ensure that queries and responses adhere to relevant regulations and ethical guidelines. They also make it possible to track and review interactions with the LLM for accountability and transparency.
Explainable responses: This aspect involves providing clear explanations for how the LLM arrived at its conclusions. This is crucial for building trust and understanding the reasoning behind the model’s outputs.
Caching: Efficient handling of similar queries. Semantic caching optimizes the LLM’s performance by storing and reusing the results of similar queries. This reduces latency and improves the overall efficiency of the system.
Hybrid search: Combining semantic and keyword matching. This technique leverages both semantic understanding and exact keyword matching to retrieve the most relevant information from the knowledge base. This approach enhances the accuracy and breadth of the LLM’s responses.
Re-ranking: Improving relevance and accuracy. Re-ranking involves retrieving a set of relevant data points and reordering them based on their relevance to the specific query. This helps ensure the most pertinent information is presented to the user.
Evals: Continuous self-learning. Evals use techniques like Reinforcement Learning from Human Feedback (RLHF) to continuously improve the LLM’s performance. This involves collecting human feedback on the model’s responses and using that feedback to refine its future outputs.
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