DSPY

RLM: Recursive Language Models for Large Codebases - Shashi, Superagentic AI

Jul 12, 2026 · 17:27

Shashi from Superagentic AI explains how Recursive Language Models (RLM), a pattern from MIT, solve the context window problem in large codebases by externalizing context management into a programmable REPL where the model writes code to inspect, slice, and compute relevant chunks, and recursively delegates sub-questions via llm_query. The RLM loop loads the repo as data, produces bounded observations, and can recursively call another model for deeper insights. Shashi demonstrates RLM Code, an open-source independent implementation running locally and on Gemini with a Docker sandbox, showing two tool calls and a complete trajectory. He notes that similar RLM concepts are used in proprietary systems like Codex, Claude managed agents, and dynamic workflows, making it a practical pattern for AI engineers dealing with monorepos.

User Signal Dies at the Retrieval Boundary - Sonam Pankaj, StarlightSearch

Jun 28, 2026 · 15:37

Sonam Pankaj, CEO of StarlightSearch, argues that production agents fail because retrieval is static and eval signals never cross into runtime context. She introduces utility-ranked memory, where memories are re-ranked by a utility score combining semantic similarity with outcome history—passing runs raise a memory's score, failing runs lower it. In a product SQL agent demo, a search for 'gaming mouse' initially failed, but after marking that output as a failure and noting a wireless mouse was relevant, the agent's trajectory updated in real time to find the correct product. Benchmarks on Tao Bench show reflect memory improves performance from 66% to 76% without skills and 80% with skills; on agentic tasks, reflect achieves 61.3% versus 35.7% baseline and 58.2% with other memory systems. Pankaj explains that after enough reviews, memories can be baked into skills, allowing continuous improvement without manual prompt rewriting or model fine-tuning.

Recursive Coding Agents - Raymond Weitekamp, OpenProse

Jun 25, 2026 · 23:48

Recursive Language Models (RLMs) are a new test-time compute paradigm that unify reasoning and tool calling, and Raymond Weitekamp of OpenProse argues they can turn coding agents into reliable, recursive problem solvers. He demonstrates that RLMs can process millions of tokens beyond context windows, and a small Qwen 3.5 9B model using RLMs beats GPT-5.4 and Opus on the LongCOT benchmark. Weitekamp shows how Claude Code's Dynamic Workflows now make it an RLM, and how OpenProse allows any coding agent to be recursively orchestrated with declared sub-agent work and dependencies. Use cases include repo-scale migrations, deep research over file systems, and adversarial red-teaming audits. He concludes that RLMs represent the next paradigm of inference-time compute, turning mismanaged genius agents into trustworthy outcomes by capturing golden sessions as reusable workflows.

Ship Real Agents: Hands-On Evals for Agentic Applications — Laurie Voss, Arize

May 14, 2026 · 2:04:18

Laurie Voss, head of developer experience at Arize AI, delivers a hands-on workshop on evaluating agentic applications using Arize Phoenix, demonstrating that choosing the right eval matters more than tuning it: a correctness eval scored 0 out of 13 on the same financial analysis agent that a faithfulness eval scored 13 out of 13, because the model doesn't know the current year and cannot verify forward-looking data. He walks through building a complete eval pipeline from scratch—starting with tracing a Claude Haiku-based financial agent, reading and categorizing traces to identify root causes, then implementing code evals, built-in LLM-as-a-judge evals, and a custom actionability rubric with labeled examples. Voss emphasizes the importance of meta-evaluation to validate judge accuracy and introduces Phoenix experiments to prove prompt changes actually improve scores, not just vibes. Practical tips include using the impact hierarchy (data quality > prompting > model selection > hyperparameters) and the value of regression evals for safe model upgrades. The workshop closes with cost-aware evaluation, pairwise evaluation, and reliability scoring as next steps beyond the foundations…

Agent Optimization with Pydantic AI: GEPA, Evals, Feedback Loops — Samuel Colvin, Pydantic

May 7, 2026 · 1:20:40

Samuel Colvin shows how to improve production agents without redeploying using Pydantic AI's GEPA, evals, and Logfire managed variables. GEPA uses a genetic algorithm to evolve prompts, achieving 96.7% accuracy — up from 87% with a simple prompt and 92% with an expert prompt — on a Wikipedia-based political relations task. Managed variables let you update prompts, models, and parameters live via a web interface and A/B test targeting. Evals compare against a golden dataset of 650 MPs; Colvin runs 65 cases in 30 seconds using GPT-4.1. He discusses that prompt optimization is most valuable with private data, that implicit user feedback (e.g., user's next action) can build golden datasets, and that overfitting to small test sets is a risk — the GEPA optimizer may exclude valid relations like 'uncle' if they don't appear in the training split.

DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners

Jan 8, 2026 · 1:13:13

Kevin Madura of AlixPartners argues that building robust enterprise AI applications requires shifting from brittle prompt engineering to programming with LLMs using DSPy, a declarative framework that treats prompts as implementation details optimized by the system. He demonstrates how typed interfaces (Signatures) and modular logic (Modules) allow developers to focus on control flow while deferring implementation to the LLM, with Adapters controlling prompt formats (e.g., JSON vs. BAML) to improve performance by 5-10%. The talk's core is Optimizers (like MIPRO and JEPA), which automatically tune prompts by learning from data, shown improving a time entry corrector from 86% to 89% accuracy. Real-world examples include routing files by type (SEC filings vs. contracts), using a 'poor man's RAG' with attachments for multimodal documents, and a boundary detector that segments legal documents from images. Madura emphasizes that DSPy enables transferability across models (e.g., GPT-4.1 to GPT-4.1 Nano) and addresses cost concerns by allowing offline optimization to reduce LLM calls.

Build a Prompt Learning Loop - SallyAnn DeLucia & Fuad Ali, Arize

Jan 6, 2026 · 52:08

SallyAnn DeLucia and Fuad Ali from Arize present prompt learning, a method to continuously improve AI prompts by using feedback from evals and human annotations. They argue that agent failures often stem from weak instructions rather than weak models, and that adding rules to system prompts can yield 15% improvements on SWE-Bench without fine-tuning or architecture changes. The talk compares prompt learning to GEBA, noting it achieves better results in fewer loops by leveraging text explanations. A case study shows Kline's performance improved from 30% to 45% on SWE-Bench-Lite after optimizing prompts. The workshop demonstrates building an optimization loop that ingests input-output pairs, evaluates them, and iteratively refines the system prompt using OpenAI.

The Unreasonable Effectiveness of Prompt Learning – Aparna Dhinakaran, Arize

Dec 23, 2025 · 10:56

Aparna Dhinakaran, co-founder of Arize, argues that prompt learning—applying RL techniques to prompts rather than model weights—can continuously improve coding agents by auto-tuning system prompts from runtime feedback. She details a process using Claude Code and CLIMB on the SWE-bench dataset: agents generate patches, unit tests yield results, then an LLM-as-a-judge eval produces natural-language explanations of failures. These explanations feed a meta prompt that iterates on the system prompt rules. On 150 SWE-bench examples, this approach improved Claude Code's issue-resolution rate by 5% and CLIMB's by 15%. She contrasts their method with DSPy's GEPA optimizer, noting theirs required fewer loops due to carefully engineered eval prompts.

Context Engineering: Connecting the Dots with Graphs — Stephen Chin, Neo4j

Nov 24, 2025 · 26:50

Stephen Chin, VP of Developer Relations at Neo4j, argues context engineering powered by knowledge graphs transforms AI from prompt engineering to information architecture, enabling agents with structured memory and retrieval. He demonstrates GraphRAG using Neo4j's knowledge graph builder with supply chain and VEX documents, showing how vector and graph algorithms retrieve specific Jackson library vulnerabilities with CVE details, severity, and remediation versions. Chin contrasts fast two-pass vector+graph retrieval with agentic multi-step traversal via the Neo4j MCP server and Claude Code, which yields deeper results like CVE number, attack type, and upgrade paths. He explains that graphs excel when relationships span two or more facts, using a presentation update example involving himself, colleague Sid, and the GIDS event. Practical applications include role-based access overlays and explainable AI via visualized conversation flows. Resources like free Graph Academy courses, Nodes AI 2026 conference, and graphrag.com support implementation.

Fuzzing in the GenAI Era — Leonard Tang, Haize Labs

Aug 22, 2025 · 19:12

Leonard Tang of Haize Labs argues that standard evaluation methods fail for GenAI systems due to brittleness (Lipschitz discontinuity), and proposes 'Haizing'—fuzz testing that simulates diverse inputs to uncover corner cases. He details two core problems: scoring outputs via 'judges', where Haize's Verdict library stacks GPT-4 Mini in a self-verified debate ensemble to beat O1 at a third the cost, and RL-tuned judges like a 1.7B parameter model achieving 80.7% on RewardBench. For stimuli generation, he frames it as discrete optimization over natural language, using gradient-based methods and tree search. Case studies include Haizing the largest Hungarian bank's loan calculator, discovering prompt injections in minutes, and boosting a voice agent's ground-truth human agreement by 38% using rubric fanout.

On Engineering AI Systems that Endure The Bitter Lesson - Omar Khattab, DSPy & Databricks

Aug 6, 2025 · 19:12

Omar Khattab, research scientist at Databricks and creator of DSPy, argues that engineering AI systems that endure the rapid pace of model and technique changes requires decoupling high-level task definitions from low-level, swappable components. He reinterprets the Bitter Lesson—that scaling search and learning beats hand-coded domain knowledge—as a warning against premature optimization, which he equates to hard-coding at lower abstraction levels than justified. Khattab criticizes prompts as a poor programming abstraction that entangles task specs, formatting, and model-specific tricks, violating separation of concerns. Instead, he advocates for investing in three orthogonal concerns: natural language specs for things that can't be otherwise expressed, evals to capture true objectives, and code for reliable structure and control flow. DSPy realizes this philosophy through declarative signatures that define what an AI system should do, independent of which LLM, inference strategy (e.g., chain-of-thought, agent), or optimizer (e.g., reinforcement learning, prompt optimization) is used. The takeaway: avoid hand engineering at lower levels than current abstractions allow, and ride…

Building Applications with AI Agents — Michael Albada, Microsoft

Jul 24, 2025 · 15:50

Michael Albada, Principal Applied Scientist at Microsoft, explains how to build effective AI agent systems, defining agents as entities that reason, act, communicate, and adapt. He emphasizes that tool use requires exposing grouped, human-facing functions rather than one-to-one API mappings, noting a 254% increase in agentic Y Combinator startups. Orchestration should start simple with chains and trees before moving to fully agentic patterns, using deterministic logic for safety and external state management. For multi-agent systems, he recommends splitting tools into semantically similar groups to avoid overwhelming a single agent. Albada stresses investing in evaluation via open-source tools like Intel Agent, Pirate, Label Studio, and automatic optimization with Trace, TextGrad, and DSPY. He warns against common pitfalls: insufficient evals, poorly designed tools, excessive complexity, and lack of observability, while urging design for security with tripwires and fallback to human review.

Prompt Engineering and AI Red Teaming — Sander Schulhoff, HackAPrompt/LearnPrompting

Jul 14, 2025 · 2:01:05

Sander Schulhoff, creator of Learn Prompting and HackAPrompt, argues prompt engineering remains vital despite claims of its demise, drawing on his systematic review of 1,500+ papers for 'The Prompt Report.' He covers advanced techniques including chain-of-thought, decomposition, ensembling, and few-shot prompting, noting that role prompting is ineffective for accuracy-based tasks and that example ordering can swing performance by 50%. He then explains AI red teaming, distinguishing jailbreaking from prompt injection, and warns that system prompts and guardrails cannot prevent attacks—even simple obfuscation like base64 or typos still works. He highlights the critical unsolved problem of agentic security, where agents with real-world actions are easily tricked, and introduces a live competition at the conference to gather more attack data. His key takeaway: AI security is fundamentally harder than classical cybersecurity because 'you cannot patch a brain.'

Engineering Better Evals: Scalable LLM Evaluation Pipelines That Work — Dat Ngo, Aman Khan, Arize

Jun 27, 2025 · 24:46

Dat Ngo, AI architect at Arize AI, presents advanced LLM evaluation strategies for production systems, arguing that effective evals go beyond out-of-the-box LLM-as-a-judge to include code-based heuristics, human feedback, and golden datasets. He explains how to build a virtuous cycle of collecting observability data, running evals, and tuning them over time, and demonstrates agent evaluation techniques like trajectory evals to identify failure modes across complex workflows. Ngo covers trade-offs between offline evals and inline guardrails, the use of log probabilities for confidence scoring, and automated prompt optimization through meta-prompting, all illustrated with customer examples from Reddit, Duolingo, and Booking.com.

Reinforcement Learning for Agents - Will Brown, ML Researcher at Morgan Stanley

Mar 7, 2025 · 18:17

Will Brown, a machine learning researcher at Morgan Stanley, argues that reinforcement learning (RL) is the essential path to unlocking autonomous AI agents, citing DeepSeek's R1 and OpenAI's Deep Research as proof: R1 used GRPO to make models learn chain-of-thought reasoning without manual data, and Deep Research applies end-to-end RL for up to 100 tool calls. He shares his own open-source single-file GRPO script—a 1B-parameter LLaMA model trained on math questions that demonstrated self-correction and improved accuracy, sparking community forks and blog posts. Brown introduces 'rubric engineering' as a new practice akin to prompt engineering, where reward rules (e.g., XML structure, integer answers) guide model improvement, and warns about reward hacking. He previews his current work: a framework for RL inside multi-step environments, letting developers reuse existing agent code for training. The talk concludes that fine-tuning and RL remain relevant as open-source catches up, and that skills like building evals and prompts translate directly to the RL era.

Optimizing LLMs in Insurance with DSPy: Jeronim Morina

Feb 16, 2025 · 19:29

Jeronim Morina from AXA Germany argues that AI engineers must stop relying on manual prompt tuning and instead adopt DSPy's automated optimization combined with first-principles thinking. He explains how his team built a customer-facing insurance chatbot by first crafting clear problem definitions, annotating evaluation data, and avoiding data leakage. After initial struggles with fragile prompts and overly complex error-handling code, they modularized their system into DSPy modules and created custom metrics for German text. Morina emphasizes that tools like DSPy require a steep learning curve and a solid baseline of hand-written prompts and basic evaluations before optimization. The episode calls for engineers to focus on real-world impact, measure everything, and break down problems into discrete, optimizable steps.

Judging LLMs: Alex Volkov

Sep 9, 2024 · 18:39

Alex Volkov, as an LLM judge from 2034, humorously judges AI engineers on their development practices, emphasizing the importance of tracing, iterative prompt engineering, and robust evaluation pipelines. He finds Daniel guilty of deploying without logging, Sasha guilty of premature fine-tuning without prompt iteration, and Morgan guilty of ignoring AI news—commuted to attending ThursdAI. Francisco’s overreliance on programmatic evals leads to a legal loss, while Maxime is 'awesome' for using Weights & Biases Weave. Alex concludes with a primer on evaluation methods: programmatic, human-in-the-loop, and LLM-as-judge, stressing the need to validate validators and create custom criteria. He promotes Weave for tracing and evals, and ThursdAI for staying updated.