Highly important tools in Ai: LangSmith, LangGraph Cloud & LangGraph Studio

" Considering the intersection of language and AI, developments have been taking place at a tremendous pace. And LangChain finds itself at the forefront of shaping how generative AI applications are developed and managed.

A few initial observations regarding generative AI and language:

1. A few months ago it was thought that OpenAI had captured the market with their highly capable LLMs.

2. Then a slew of open-sourced models, most notably from Meta disrupted the perceived commercial model.

3. LLM providers realized that Language Models would become a mere utility and started to focus on end-user applications and RAG-like functionalities referred to as grounding, agent-like functionality and personal assistants.

4. Hallucination had to be solved for, and it was discovered that LLMs do not have emergent capabilities, but rather LLMs do exceptionally well at in-context learning (ICL). An application structure developed around implementing, scaling and managing ICL implementations; which we now know as RAG.

5. RAG (non-gradient) started to be preferred above fine-tuning (gradient) approaches for reasons of being transparent, not as opaque as fine-tuning. Adding to generative AI apps being observable, inspectable and easy modifiable.

6. Because we started using all aspects of LLMs (NLG, reasoning, planning, dialog state management, etc.) except the knowledge intensive nature of LLMs, Small Language Models become very much applicable.

7. This was due to very capable open-sourced SLMs, quantization, local, offline inference, advanced capability in reasoning and chain-of-thought training.

8. And, the focus is shifting to two aspects...the first being a data centric approach. Where unstructured data can be discovered, designed and augmented for RAG and fine-tuning. Recent fine-tuning did not focus on augmenting the knowledge-intensive nature of Language Models, but rather to imbue the LMs with specific behavioral capabilities.

9. This is evident in the recent acquisition by OpenAI to move closer to the data portion and delivering RAG solutions.

10. The second aspect is the need for a no-code to low-code AI productivity suite providing access to models, hosting, flow-engineering, fine-tuning, prompt studio and guardrails.

11. There is also a notable movement to add graph data...graph is an abstract data type...An abstract data type is a mathematical model for data types, defined by its behavior (semantics) from the point of view of a user of the data. Abstract data types are in stark contrasts with data structures, which are concrete representations of data, and are the point of view of an implementer, not a user. This data structure is less opaque and easy to interpret. "

Author

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