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A Step-By-Step Guide To Powering Your Application With LLMs
- GenAI has real-world applications and generates revenue for companies, leading to heavy investments in research.
- Before powering an application with Large Language Models (LLMs), define the use case clearly and assess resource availability.
- Choose between training a model from scratch or using pre-trained models like 1B, 10B, or 100B+ parameter models based on specific use cases.
- Enhance the model by providing additional context using methods like prompt engineering or reinforced learning with human feedback.
- Evaluate the model manually or using metrics like ROUGE scores to ensure proper functioning and extract performance insights.
- Optimize the model by quantizing weights and pruning to reduce memory requirements, computing costs, and improve performance.
- Pre-trained models like ChatGPT and FLAN-T5 can be utilized and fine-tuned, followed by deployment for application use cases.
- Powering applications with LLMs involves a step-by-step process from defining use cases to optimizing and deploying models.
- The process includes choosing the right model, enhancing it with additional data, evaluating performance, and fine-tuning for efficient deployment.
- Utilizing techniques like RAG and metrics like ROUGE scores can ensure model effectiveness and alignment with application requirements.
- Overall, leveraging LLMs for applications requires strategic planning, evaluation, optimization, and deployment to maximize performance.
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