When integrating internal knowledge into AI applications, three main approaches stand out:
1. Prompt Context Load all relevant information into the context window and leverage prompt caching. 2. Retrieval-Augmented Generation (RAG) Use text embeddings to fetch only the most relevant information for each query. 3. Fine-Tuning Train a foundation model to better align with specific needs.
Each approach has its own strengths and trade-offs:
Hi everyone, Dani from Jam here! We just launched Jam AI yesterday (producthunt.com/posts/jam-ai) and are super excited to see it's fully writing ~30% of bug reports on its first day! I'm here to answer any questions about the future of AI + coding/debugging, building AI products, reporting bugs, and on building startups in general!
Hi everyone, Dani from Jam here! We just launched Jam AI yesterday (producthunt.com/posts/jam-ai) and are super excited to see it's fully writing ~30% of bug reports on its first day! I'm here to answer any questions about the future of AI + coding/debugging, building AI products, reporting bugs, and on building startups in general!
