LLMs Smarter + Faster + Better = PDL (IBM Research)

The Prompt Declaration Language (PDL) is introduced as a declarative, YAML-based language that aims to simplify and optimize prompt programming for language models (LLMs). PDL is designed to overcome limitations in current prompting frameworks, which either require high technical expertise or limit developer control over prompts.

By focusing on a data-oriented, orthogonal structure, PDL allows developers to compose reusable prompt patterns, such as chaining, retrieval-augmented generation, and agent-based workflows, in a highly modular way. Each PDL program is represented as a series of YAML blocks, enabling the flexible combination of various prompt modules without requiring complex imperative code. This approach improves the modularity and readability of LLM-based applications and facilitates integration across different LLM platforms.

PDL also includes control constructs like loops, conditionals, and function definitions, enabling sophisticated program structures that build dynamic, context-driven prompts for interactive applications, such as chatbots and tools. Through its integration of JSON Schema-based type-checking, Jinja2 templating, and interpreter capabilities for handling control flow and context, PDL creates structured, maintainable prompts that minimize the brittleness typically associated with free-form text input in LLMs.

Enhanced Prompt Control: PDL (Prompt Declaration Language) allows developers to craft more precise, context-aware prompts for LLMs, moving beyond basic prompt engineering. With structured data and control over variables, developers can guide the model's responses more intelligently, reducing ambiguity and enhancing relevance.

Dynamic Context Management: PDL’s use of YAML blocks and a declarative style means that prompt instructions can be modular and reusable. This enables complex, layered conversations where the model maintains context across stages, refining its responses based on previously gathered information.

Integration with Tools and Agents: By enabling LLMs to interface with retrieval-augmented generation (RAG), chatbots, and agent-based workflows, PDL empowers models to perform multi-step tasks efficiently, mimicking human-like reasoning in complex problem-solving.

All rights w/ authors:
PDL: A Declarative Prompt Programming Language
https://arxiv.org/pdf/2410.19135

00:00 No Tools, just LLM Intelligence
01:30 OpenAI Prompt Generator Code
03:04 LLM Prompt Programs (Stanford)
08:39 Key Insights
09:40 DSPy code for Multi-Agents
10:40 Reverse engineer o1
15:49 Prompt Programming Language PDL
19:23 PDL is open-source
22:05 Chatbot in PDL code
23:19 RAG in PDL code
25:14 Relational programming with Foundation
27:47 IBM Granite 3.0 Models
29:19 DSPy 3.0 coming soon
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