Grounding can be achieved through methods like RAG, which can be constructed in various ways. To stay updated with state-of-the-art prompting techniques and related publications, resources like arxiv.org are recommended. This includes publications on language model learning, scaling, and other relevant topics.
Key takeaways:
- Prompt Engineering is a significant aspect of working with Language Learning Models (LLMs), and modifying a prompt can significantly improve the output.
- There is a need for a systematic way to characterize the differences in outputs based on different prompts.
- Regular evaluation of new models against existing ones like gpt-2, gpt-3.5-turbo, etc, is important to assess their performance.
- For grounding, RAG can be built in various ways and staying updated with publications on arxiv.org can help stay on top of State Of The Art (SOTA) prompting techniques.