Thanks to Sponsor Pegasus One for this thought leadership article outlining best practices for startup software products:

The Path to Deploying Production-Quality Generative AI Applications

Generative AI (GenAI) has revolutionized the technological landscape, offering unprecedented opportunities for businesses to innovate and optimize their operations.

Despite its potential, many organizations face challenges in deploying production-quality GenAI applications. To achieve high standards of quality, accuracy, governance, and safety, a comprehensive understanding of the GenAI process and its components is essential.

Stage 0: Foundation Models

Foundation models, which are large language models (LLMs) trained on extensive datasets, serve as the cornerstone for building advanced GenAI applications. These models can be proprietary, like GPT-3.5 and Gemini, or open source, such as Llama2-70B. Proprietary models often offer superior performance but come with constraints related to data privacy and control. In contrast, open-source models provide users with greater control and governance, allowing them to customize and optimize the models according to their specific needs.

Stage 1: Prompt Engineering

Prompt engineering is the practice of designing and refining prompts to elicit the best possible responses from LLMs. This stage is crucial for optimizing the performance of GenAI applications, ensuring that the generated outputs are relevant and accurate.

Use Case: Automated Analysis of Product Reviews By leveraging prompt engineering, businesses can use LLMs to gain actionable insights from product reviews. This involves creating tailored prompts that guide the LLM to extract meaningful information from large datasets of customer feedback.

Stage 2: Retrieval Augmented Generation (RAG)

RAG combines the capabilities of retrieval-based and generation-based models to enhance the quality and relevance of the generated content. It involves retrieving relevant documents or information and using them to generate more accurate and contextually appropriate responses.

Use Case: Improving Chatbot Responses Implementing …

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