Cache Layers

The Retrieval-Augmented Generation (RAG) pipeline is distinguished by its layered caching mechanism, purposefully designed to deliver consistent outputs, avoid hallucinations, and operate with enhanced speed. This multi-layered approach is critical in handling the complexity and diversity of queries encountered by AI systems. By integrating different cache layers into the RAG system, we ensure that each query is addressed with the appropriate level of analysis and resource utilization, thereby optimizing both performance and cost.

Name	Speed	Cost	LLM Calls
L1: Direct Hit			0
L2: Similarity Check			N
L3: Abstract Memory Recall			N^2
L4: Ai agent with tools			???
L5: Ai agent with reinforcement learning			???

L1: Direct Hit

The first layer, Direct Hit, is engineered for speed and precision. When an identical query is submitted, this layer swiftly retrieves the answer by accessing a database of past queries and their corresponding medical codes. This process sidesteps the more time-consuming language processing stages, delivering instant results with remarkable accuracy.

L2: Similarity check

At the second layer, Similarity Check, the system employs cosine similarity against a vector database filled with past successful queries and their codes. By evaluating the similarity of new queries to those in its memory, it identifies the most relevant responses. Each candidate is then verified through a GPT prompt, ensuring that only the most accurate codes are selected for reuse.

L3: Abstract Memory Recall

The third layer, Abstract Memory Recall, also utilizes cosine similarity but adds a layer of complexity by combining codes from multiple related queries. This enables the AI to tackle more complex questions by synthesizing elements from various relevant past queries. For instance, if separate queries about patients with lung cancer and patients over 18 were successfully answered in the past, Abstract Memory Recall can amalgamate these snippets to address a compound query regarding patients with lung cancer who are also over 18.

L4: Ai agent with tools

Moving to the fourth layer, the AI Agent with Tools, we see an advancement in the AI's ability to integrate and apply various tools and skills. This layer not only retrieves and combines relevant code snippets but also enhances its responses by leveraging additional AI capabilities. This integrated approach allows the AI to solve queries more comprehensively. Idealy, we will run this version ourselves, pre-populating the long term memory with insightful queries.

L5: Ai agent with reinforcement learning

The fifth layer of the Retrieval-Augmented Generation (RAG) pipeline, involving an AI Agent with Reinforcement Learning, is designed to optimize query handling and improve the accuracy of responses over time. This process of continuous improvement is achieved through a methodical approach that allows the AI to learn from each interaction. By running the agent on a question multiple times, the system can explore a variety of solutions, evaluate their effectiveness, and ultimately select the best response. Here's an overview of how this process works:

1. Exploration and Exploitation

The AI agent operates on the principles of exploration and exploitation. Exploration allows the agent to try different responses to the same question, thereby gathering data on the effectiveness of each attempt. Exploitation involves using the knowledge gained from past interactions to select the response that is most likely to be correct or helpful. Over multiple iterations, the balance between exploration and exploitation shifts towards exploitation as the agent becomes more confident in its choices.

2. Reward Mechanism

At the heart of reinforcement learning is a reward mechanism that evaluates the effectiveness of each response. After the AI agent proposes a solution to a query, it receives feedback that quantifies how well the response met the query's requirements. This feedback can be based on various factors, such as accuracy, relevance, and completeness. The reward mechanism helps the agent to understand which types of responses are more effective, guiding its learning process.

3. Iterative Learning

By running the agent on a question multiple times, it iteratively learns which responses yield the highest rewards. Each iteration provides the agent with valuable feedback that it uses to adjust its strategy. This iterative learning process is crucial for refining the agent's ability to generate accurate and relevant responses.

4. Policy Optimization

The AI agent uses a policy, which is essentially a strategy for selecting responses based on the current state of knowledge. As the agent iterates over a question, it optimizes this policy to increase the likelihood of selecting the best possible solution. Policy optimization is a critical component of reinforcement learning, enabling the agent to improve its decision-making process over time.

5. Selection of the Best Solution

After running through multiple iterations and receiving feedback for each, the agent uses its optimized policy to select the best solution. This solution is the one that the agent predicts will yield the highest reward based on its accumulated knowledge. The selection process benefits from the agent's improved understanding and adaptability, ensuring that the chosen response is tailored to the nuances of the query.