Natural Language Processing with OpenAI and Amazon Bedrock

By removing unnecessary tokens from the dataset, reducing the size of the training data, and thus improving the model's performance.

To save cost and time by delivering responses from cache for repetitive prompts instead of calling the LLM every time.

By collecting user feedback and monitoring the percentage of positive/negative feedback, providing a report for better decision making.

<p>To cleanse the input data by removing profanity, filtering, and refraining from unnecessary data before sending it to LLMs.</p> Signup and view all the answers

<p>By providing a framework to connect to different data stores, such as graph databases like Neo4J, and vector databases like Pinecone and Chroma.</p> Signup and view all the answers

<p>To evaluate the accuracy of one LLM by using another LLM as a benchmark, which helps in model governance and improvement.</p> Signup and view all the answers

<p>By reducing the cost associated with LLM deployment, through strategies such as caching and token count optimization.</p> Signup and view all the answers

<p>Vector databases, such as Pinecone and Chroma, facilitate connection with unstructured data, enabling RAG on a wide range of data types.</p> Signup and view all the answers

<p>By generating code and automating tasks, the multi-agent framework improves developer productivity and reduces the time and effort required for LLM deployment.</p> Signup and view all the answers

<p>Persona-based Q&amp;A enables personalized and relevant content generation, by adapting to the user's persona and preferences.</p> Signup and view all the answers