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Challenges and Opportunities Building LLM-Powered Application - Sachin Solkhan
Discover the challenges and opportunities of building LLM-powered applications, including the importance of data curation, model size, and evaluation techniques, as well as enterprise considerations for large language models.
- The RAG approach is better for accuracy and usefulness, as it allows for fine-tuning and uses the latest data.
- The size of the models is important, with larger models like GPT-4 being more powerful but also more expensive.
- There are multiple steps in getting the data, including data curation, data processing, and training on large datasets.
- There are various ways to generate prompts, including natural language and other constructs.
- Consistency and accuracy are important for evaluating the output of large language models.
- Hallucinations need to be considered and addressed through techniques like RAG and fine-tuning.
- The cost of running large language models is important, with different models and approaches having different costs.
- Enterprise considerations are important, including bias, toxicity, and compliance.
- Orchestration frameworks are necessary for integrating and using large language models.
- Fine-tuning and RAG are important techniques for improving the accuracy and usefulness of large language models.
- Domain-specific large language models are more suitable for certain domains and tasks.
- There are many different approaches to large language models, including RAG, fine-tuning, and domain-specific models.