Your system of work and the importance of grounded composability in GenAI solutions

Applied Generative AI (GenAI) is pressing forward beyond “chat apps” for question and response with language completion.  The latest large language models (LLMs) and GenAI solutions lean in toward a system of work beyond simple tasks which incorporates knowledge, reasoning, and action.

 DSPy, is a framework for programming—not prompting—and designing AI systems using pipelines of trained language models (LM) and other tools. DSPy is a powerful and versatile solution that bridges the gap between language models (LMs) and retrieval models (RMs). Imagine it as a Langchain or Llama chain replacement. Where its strengths lie in its inclusivity and ability to provide end-to-end solutions. It also enables reasoning without deep and complex prompt engineering. Let’s delve into what makes DSPy unique:

• DSPy seamlessly unifies techniques for prompting and fine-tuning LMs. It also integrates approaches for reasoning, self-improvement, and augmentation using retrieval tools. All these capabilities are expressed through modular beyond abstraction components that learn and adapt.
• Pythonic Syntax: DSPy offers composable and declarative modules that instruct LMs in a familiar Pythonic syntax. Gone are the days of hand-adapted string manipulation tricks; instead, you can explore a systematic space of modular and trainable pieces.
• Automatic Compilation: The DSPy compiler is a game-changer. It traces your program and crafts high-quality prompts for large LMs or trains automatic finetunes for smaller ones. No manual labels needed—DSPy bootstraps prompts and finetunes from minimal data, making it robust and efficient.
• Reliable Models: For complex tasks, DSPy can routinely teach powerful models like GPT-3.5 and local models like T5-base or Llama2-13b to be much more reliable. It compiles the same program into different few-shot prompts and/or finetunes for each LM, adapting dynamically to your changes.

 DSPy empowers you to build sophisticated language-driven applications, making it an essential tool for developers and researchers alike.

 Language models like LLMs lack the ability to access personal information or specific details about individuals beyond what has been publicly shared. As reasoning improves, acting is gaining importance. The ability to act and interact with automation leads to agency —an agent that acts. We see these as “agents” and “assistants” and we can think of them as functions. But how do we know what to do? And how can we inform the GenAI solution beyond the LLM and expensive fine-tuning?

 LLMs do not know your data and the public foundational models (FM) should not be trained on your data. Grounding ties our efforts to known good data.

 Retrieval Augmented Generation (RAG) plays a crucial role in grounding LLMs, providing factual context and enhancing their responses. RAG fits into the needed grounding and implementation, using vector databases and knowledge graphs.

 RAG augments LLMs by providing grounding data from an external knowledge base. This grounding context ensures that LLM responses are based on accurate, up-to-date information retrieved from external sources. Each component is essential. In  GenAI, the LM is responsible for formulating responses. Information Retrieval System provides grounding data to the LLM. RAG provides control and allows you to constrain generative AI to your enterprise content, including vectorized documents, images, and other data formats if you have embedding models for that content. 

 RAG can use Vector Databases and Knowledge Graphs. Vector databases store embeddings (vector representations) of documents or content. Using vector databases ensures that the grounding data is accurate and relevant. It provides a reference point for users to verify the generated information. Knowledge graphs organize structured information about entities, relationships, and facts. Knowledge graphs offer a rich context for grounding LLMs. They capture semantic relationships between entities. Knowledge graphs can leverage ontologies.

 RAG is about agency. But agency goes further. Let’s explore how RAG supports both Chain of Thought (CoT) and ReAct in terms of grounding.

 Chain of Thought (CoT) primarily emphasizes reasoning. It allows LLMs to carry out reasoning traces for various tasks, including arithmetic and commonsense reasoning. CoT is effective for solving specific problems such as generating answers through reasoning. CoT has its limitations. It lacks access to the external world. It also has an inability to update its knowledge which can lead to issues like fact hallucination and error propagation. CoT provides a solid foundation for reasoning within the DSPy framework.

 ReAct combines both reasoning and acting. ReAct prompts LLMs to generate verbal reasoning traces and actions for a given task. ReAct enables dynamic reasoning by creating, maintaining, and adjusting plans for acting. It allows interaction with external environments (e.g., Wikipedia) to incorporate additional information into the reasoning process. This improves human interpretability and trustworthiness of LLMs. ReAct outperforms several state-of-the-art baselines on language and decision-making tasks. It allows LLMs to retrieve external information for more reliable and factual responses. 

Example: Given a question, ReAct generates task-solving trajectories by combining reasoning and action steps.

ReAct enhances the capabilities of DSPy by integrating reasoning and acting, leading to more robust and context-aware responses.

 While CoT and ReAct both contribute to reasoning, ReAct’s inclusion of action and observation makes it suitable for learning through interaction with the environment. Leveraging both CoT and ReAct within DSPy can lead to more powerful and contextually aware solutions.

Let’s explore how the limitations of Chain of Thought (CoT) and ReAct can be mitigated or improved. 

Chain of Thought (CoT) has a lack of External World Access.To address this limitation, CoT could be enhanced by incorporating external knowledge sources. A solution could I ntegrate CoT with knowledge graphs (e.g., Wikidata) to provide factual information. Another way would be to allow CoT to perform web searches to retrieve up-to-date data. Enable Dynamic Knowledge Update. Implement mechanisms to periodically update CoT’s knowledge base to prevent fact hallucination and then regularly validate and verify the information within CoT. A solution could also combine CoT with other reasoning paradigms (e.g., neural-symbolic approaches) to leverage the strengths of both. Such hybrid models can address CoT’s limitations while maintaining its reasoning capabilities.

ReAct could be enhanced to improve Observation and Interaction by enabling more sophisticated observation and interaction with the environment. Provide attention mechanisms to improve the model’s ability to focus on relevant parts of the environment. Incorporate memory networks to store and retrieve relevant context. Integrate ReAct with external data sources (e.g., APIs, databases) to enhance its knowledge base and to use real-time information for more accurate responses. Apply Adaptive Planning by. Developing mechanisms for ReAct to adapt its plans dynamically based on changing circumstances. Consider reinforcement learning techniques to improve planning and decision-making. Involve Human-in-the-Loop for feedback to validate ReAct’s actions and reasoning. Finally, fine-tune ReAct based on user feedback to improve its performance.

Addressing these limitations through external knowledge, dynamic updates, hybrid approaches, improved observation, adaptive planning, and human collaboration can significantly shore up the capabilities of both CoT and ReAct in the context of DSPy .

RAG and Chain of Thought (CoT) provide grounding. CoT focuses on reasoning, but it lacks direct access to the external world. RAG bridges this gap by retrieving information from external sources. When CoT generates responses, RAG ensures they are grounded in factual data. RAG allows CoT to dynamically update its knowledge base. By incorporating real-time information, CoT can avoid fact hallucination and provide accurate answers. CoT combined with RAG forms a powerful hybrid model. CoT’s reasoning abilities complemented by RAG’s grounding lead to more reliable and context-aware responses.

Using RAG and ReAct together also provides grounding. ReAct combines reasoning and acting, making it context aware. RAG ensures that ReAct’s actions and reasoning are based on factual data. When ReAct interacts with external environments (e.g., Wikipedia), RAG provides the necessary grounding. RAG supports ReAct’s observation mechanisms. By retrieving relevant context from external sources, ReAct can make informed decisions. RAG allows ReAct to validate its actions through external data. Human feedback can further fine-tune ReAct’s performance.

Summary

RAG acts as the bridge between LLMs, and frameworks such as CoT and ReAct, and the external world. Whether it’s reasoning or decision-making, RAG ensures that responses are reliable, factual, and contextually grounded. The composable ensemble is what makes for a better solution.

As reasoning and taking action continue to advance these components and frameworks are essential to getting work done. DSPy is one method for delivering both reasoning and action. This is also being built out by the major LLM vendors and hyperscalers. A great solution gives you the flexibility to migrate, swap, or add new components in your ensemble of composability.

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