Topic Brief: Planning, Reasoning, and Agents Reading Group 2026-01-14 meeting recording. llm In DSPy, you only need to declare the required "Natural Language ...

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llm In DSPy, you only need to declare the required "Natural Language ... Planning, Reasoning, and Agents Reading Group 2026-01-14 meeting recording. Prompt engineering doesn't scale—especially when models change, prompts drift, and your “logic” lives inside a giant string.

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Prompt engineering doesn't scale—especially when models change, prompts drift, and your “logic” lives inside a giant string.

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  • Prompt engineering doesn't scale—especially when models change, prompts drift, and your “logic” lives inside a giant string.
  • Planning, Reasoning, and Agents Reading Group 2026-01-14 meeting recording.
  • llm In DSPy, you only need to declare the required "Natural Language ...

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Image-Based Context

GEPA Explained!
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GEPA: A New LLM Prompt Optimizer Beats RL
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GEPA with Lakshya A. Agrawal - Weaviate Podcast #127!
GEPA Explained: How LLMs as Optimizers Outperform Reinforcement Learning (RL) | Prompt Engineering
[Paper Reading]: GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
DSPy 3 + GEPA: The Most Advanced RAG Framework Yet — Auto Reasoning & Prompting
Planning, Reasoning, and Agents RG, 2026-01-14 Session: GEPA, prompt optimization outperforming RL.
GEPA: Reflective Prompt Evolution and Pareto-Based Optimization
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GEPA Explained!

GEPA Explained!

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Matei Zaharia - Reflective Optimization of Agents with GEPA and DSPy

Matei Zaharia - Reflective Optimization of Agents with GEPA and DSPy

Read more details and related context about Matei Zaharia - Reflective Optimization of Agents with GEPA and DSPy.

GEPA: A New LLM Prompt Optimizer Beats RL

GEPA: A New LLM Prompt Optimizer Beats RL

In this AI Research Roundup episode, Alex discusses the paper: '

DSPy Explained (Databricks Demo): Build Model-Agnostic Agents + Auto Prompt Optimization (GEPA)

DSPy Explained (Databricks Demo): Build Model-Agnostic Agents + Auto Prompt Optimization (GEPA)

Prompt engineering doesn't scale—especially when models change, prompts drift, and your “logic” lives inside a giant string.

GEPA with Lakshya A. Agrawal - Weaviate Podcast #127!

GEPA with Lakshya A. Agrawal - Weaviate Podcast #127!

Lakshya A. Agrawal is a Ph.D. student at U.C. Berkeley! Lakshya has lead the research behind

GEPA Explained: How LLMs as Optimizers Outperform Reinforcement Learning (RL) | Prompt Engineering

GEPA Explained: How LLMs as Optimizers Outperform Reinforcement Learning (RL) | Prompt Engineering

Read more details and related context about GEPA Explained: How LLMs as Optimizers Outperform Reinforcement Learning (RL) | Prompt Engineering.

[Paper Reading]: GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

[Paper Reading]: GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Read more details and related context about [Paper Reading]: GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning.

DSPy 3 + GEPA: The Most Advanced RAG Framework Yet — Auto Reasoning & Prompting

DSPy 3 + GEPA: The Most Advanced RAG Framework Yet — Auto Reasoning & Prompting

llm In DSPy, you only need to declare the required "Natural Language ...

Planning, Reasoning, and Agents RG, 2026-01-14 Session: GEPA, prompt optimization outperforming RL.

Planning, Reasoning, and Agents RG, 2026-01-14 Session: GEPA, prompt optimization outperforming RL.

Planning, Reasoning, and Agents Reading Group 2026-01-14 meeting recording. Dzmitry Pletnikau presents the paper ...

GEPA: Reflective Prompt Evolution and Pareto-Based Optimization

GEPA: Reflective Prompt Evolution and Pareto-Based Optimization

Read more details and related context about GEPA: Reflective Prompt Evolution and Pareto-Based Optimization.