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Generative AI has high utility for generating metadata for both structured and unstructured data and is relevant in the storage domain where data discoverability drives the value of data across the enterprise including for downstream AI projects.

Operating AI across different clouds and execution engines becomes complex and difficult to maintain with cloud native tools as the number of different integrations between systems proliferates at scale. OpenShiftAI provides a cohesive hybrid, multi-cloud AI platform that enables enterprises to separate concerns between pipeline orchestration and workload execution reducing complexity in the data and governance subdomains and enabling enterprises to operate AI at scale.

The watsonx.ai BaseChatModel supports integration with LangChain for building LangChain Tool-Agents. The following code demonstrates use of the LangChain watsonx BaseChatModel to construct a Tool-Agent. The application logic follows: (1) a call to the language model to determine which tools to invoke; (2) the programmatic invocation of the selected tools (3) a final call to the watsonx language model with the response from the tools.

Q*, a hybridisation of Q-learning and the pathfinding algorithm A*, has the potential to enhance the inductive bias of a language model in tasks that demand certain types of reasoning. An implementation of Q* is described here https://lnkd.

Whilst Monti Carlo Tree Search and Q* are promising approaches for aligning and guiding general purpose language models in a specialised domain, MLOps (or LLMOps) remains essential for maintaining models that are susceptible to drift.

A key challenge in language model applications is managing the dependency on language model prompts. Changes to the data pipeline, the model or the data requires prompts to be re-optimised. DSPy is a framework for algorithmically optimizing LM prompts and weights that separates the flow of a language model application from the parameters (LM prompts and weights) of each step and provides LM-driven algorithms that can tune the prompts and/or the weights of your LM calls, given a metric you want to maximize.

NeMo-Guardrails is an open-source toolkit that allows developers to add programmable guardrails semantically matched on utterances to LLM-based conversational applications. NeMo-Guardrails can be easily integrated with watsonx.ai models using LangChain’s WatsonxLLM Integration.

A common use case for conversational assistants is generating conversational responses to questions users ask of some source information. A common pattern is to retrieve relevant context through semantic search and to pass that context to the language model in the prompt, aligning the language model around a contextualised response.

Effective governance of enterprise services and applications that utilise generative models requires a multi-layered approach of different classifiers that guardrail the inputs to and outputs from generative models. These models, which are called synchronously by the application and drive application logic and consumed via an API, abstracted away through an SDK or inferenced directly, must themselves be governed.

Building user journeys as declarative trees within a virtual assistant requires assumptions to be made about the user query and the optimal path. If there are many decision points and the tree consists of many forks the number of assumptions increases exponentially down the tree leading to inefficiencies and a suboptimal design.