Tag

practical-ai

55 posts

May 11, 2026 Office Hours

Office Hours — Should you use agentic search or RAG for retrieval, and what's the tradeoff in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 10, 2026 Office Hours

Office Hours — How are you preventing runaway LLM workflows and token costs in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 9, 2026 Office Hours

Office Hours — How are you structuring context and prompts for AI coding agents to get reliable results?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 8, 2026 Office Hours

Office Hours — What breaks when you run AI agents unsupervised in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 7, 2026 Office Hours

Office Hours — How are you using LLMs in production and what unexpected issues have you hit?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 7, 2026 Paper of the Week

Paper of the Week — TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments

TSCG shows small LLMs (4–14B) drop tool-call failures by compiling JSON schemas into natural-language descriptions before inference.

research papers arxiv practical-ai
May 6, 2026 Office Hours

Office Hours — Are you using finetuning for LLM agents in production, and if so, what trade-offs did you encounter versus using base models with prompt engineering?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 4, 2026 Office Hours

Office Hours — How should you structure memory and context for AI agents so they can learn from past tasks without growing unbounded token usage?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 3, 2026 Office Hours

Office Hours — What monitoring and safeguards do you need in place to control AI agents that take real actions in production systems?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 2, 2026 Office Hours

Office Hours — How do you keep AI coding agents aligned with your team's codebase standards, style guides, and architectural decisions?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
May 1, 2026 Office Hours

Office Hours — Should you give AI agents access to API keys and private credentials, and if so, what isolation strategies actually work?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 30, 2026 Office Hours

Office Hours — What's the best way to set cost limits and prevent AI agents from burning through your API budget on failed or inefficient tasks?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 30, 2026 Paper of the Week

Paper of the Week — Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use

Rewriting tool descriptions at deployment time—not training time—can recover 20-40% of function-calling accuracy lost to poorly written API docs.

research papers arxiv practical-ai
Apr 29, 2026 Office Hours

Office Hours — How do you prevent AI agents from accidentally introducing vulnerable dependencies or malicious code when they autonomously modify your codebase?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 27, 2026 Office Hours

Office Hours — What's your strategy for testing and evaluating LLM outputs in production now that Promptfoo was acquired?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 26, 2026 Office Hours

Office Hours — Has anyone successfully fine-tuned LLMs for production use and what was the ROI?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 25, 2026 Office Hours

Office Hours — Is operational memory a missing layer in AI agent architecture?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 24, 2026 Office Hours

Office Hours — What hiring criteria should you use when your team is heavily using AI-assisted coding?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 23, 2026 Office Hours

Office Hours — Who is actually getting measurable value from AI agents in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 23, 2026 Paper of the Week

Paper of the Week — Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations

Visualizing LLM output distributions reveals hidden modes, edge cases, and prompt sensitivity that single-sample evaluation completely misses.

research papers arxiv practical-ai
Apr 22, 2026 Office Hours

Office Hours — How do you know if AI agents will choose your tool?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 21, 2026 Office Hours

Office Hours — Has anyone deployed LLMs to production and what were the biggest operational challenges?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 20, 2026 Office Hours

Office Hours — How are you extracting the best performance out of your RAG pipeline?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 19, 2026 Office Hours

Office Hours — Is anyone using function calling with LLMs in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 18, 2026 Office Hours

Office Hours — Best LLM stack for Q&A over internal PDFs?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 17, 2026 Office Hours

Office Hours — What tools are you using for AI evals, and why does everything feel half-baked?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 17, 2026 Paper of the Week

Paper of the Week — Lossless Prompt Compression via Dictionary-Encoding and In-Context Learning: Enabling Cost-Effective LLM Analysis of Repetitive Data

Lossless prompt compression via dictionary encoding lets LLMs analyze repeated data at a fraction of token cost — no external tools, just in-context learning.

research papers arxiv practical-ai
Apr 16, 2026 Office Hours

Office Hours — Is synthetic data generation practical outside academia?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 14, 2026 Office Hours

Office Hours — Are we pretending RAG is ready, when it's barely out of demo phase?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 13, 2026 Office Hours

Office Hours — Why don't we have a functional DSL for data+embedding+API pipelines?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 12, 2026 Office Hours

Office Hours — Why are so many companies rolling out their own AI/LLM agent sandboxing solution?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 11, 2026 Office Hours

Office Hours — What are some actual use cases of AI Agents right now?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 10, 2026 Office Hours

Office Hours — Can you personalize an LLM by fine-tuning it on conversation history, or is that the wrong approach?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 9, 2026 Office Hours

Office Hours — Who is actually getting real business value from AI agents right now, and how?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 9, 2026 Paper of the Week

Paper of the Week — Mamba-Based State Space Models for Long-Context Retrieval-Augmented Generation

Structured state-space models finally beat transformers at document retrieval — here's what the Mamba-based RAG benchmark actually shows.

research papers arxiv practical-ai
Apr 8, 2026 Office Hours

Office Hours — Is fine-tuning LLMs actually worth it, or is prompt engineering and RAG always the better path?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 7, 2026 Office Hours

Office Hours — Is fine-tuning the right approach when you want a model to reliably remember your codebase or company-specific data?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 6, 2026 Office Hours

Office Hours — What kinds of tasks are AI agents actually reliable at in production today?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 5, 2026 Office Hours

Office Hours — Is RAG an antipattern for AI agents, or is it still the right default approach?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 4, 2026 Office Hours

Office Hours — How do you actually monitor AI agents in production when there's no standard playbook yet?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 3, 2026 Office Hours

Office Hours — We're using an LLM to extract structured data from messy PDFs. Sometimes it works perfectly, sometimes it misses fields or invents data. How do I know if the problem is the model, my prompt, or the PDF quality itself?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 2, 2026 Office Hours

Office Hours — We're getting inconsistent outputs from the same prompt with GPT-5.4. Temperature is locked at 0. What's actually going on?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Apr 2, 2026 Paper of the Week

Paper of the Week — SnapKV: LLM Knows What You are Looking for Before Generation

KV cache compression that cuts memory 40–60% with under 1% accuracy loss — here's the technique your inference stack probably isn't using yet.

research papers arxiv practical-ai
Apr 1, 2026 Office Hours

Office Hours — I'm using Claude Opus 4.6 for a customer-facing summarization task. Should I batch requests during off-peak hours to save money, or just call the API in real-time?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 31, 2026 Office Hours

Office Hours — How do I know when to stop prompt engineering and just upgrade my model?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 30, 2026 Office Hours

Office Hours — Is it better to improve the harness around the LLM or wait for a better model?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 29, 2026 Office Hours

Office Hours — Should I A/B test my LLM prompts in production or is that overkill?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 28, 2026 Office Hours

Office Hours — What's the hardest part of building AI agents that actually work?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 27, 2026 Office Hours

Office Hours — How do you actually test LLM apps beyond vibe checks?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 26, 2026 Office Hours

Office Hours — Why is AI agent reliability barely improving despite 18 months of model upgrades?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 26, 2026 Paper of the Week

Paper of the Week — Training Language Models to Self-Correct via Reinforcement Learning

SCoRe trains a single LLM to catch and fix its own mistakes via RL — 15.6% better on math, 9.1% on code, no multi-model pipeline needed.

research papers arxiv practical-ai
Mar 25, 2026 Office Hours

Office Hours — How are people safely reusing cached LLM answers in production RAG systems?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 24, 2026 Office Hours

Office Hours — Do structured outputs from LLMs create false confidence that the response is actually correct?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 23, 2026 Office Hours

Office Hours — How are you handling LLM API costs in production without sacrificing quality?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai
Mar 22, 2026 Office Hours

Office Hours — How do I actually know if my LLM is hallucinating in production?

A daily developer question about AI/LLMs, answered with a direct, opinionated take.

office-hours q-and-a practical-ai

Office Hours — Should you use agentic search or RAG for retrieval, and what's the tradeoff in production?

Office Hours — How are you preventing runaway LLM workflows and token costs in production?

Office Hours — How are you structuring context and prompts for AI coding agents to get reliable results?

Office Hours — What breaks when you run AI agents unsupervised in production?

Office Hours — How are you using LLMs in production and what unexpected issues have you hit?

Paper of the Week — TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments

Office Hours — Are you using finetuning for LLM agents in production, and if so, what trade-offs did you encounter versus using base models with prompt engineering?

Office Hours — How should you structure memory and context for AI agents so they can learn from past tasks without growing unbounded token usage?

Office Hours — What monitoring and safeguards do you need in place to control AI agents that take real actions in production systems?

Office Hours — How do you keep AI coding agents aligned with your team's codebase standards, style guides, and architectural decisions?

Office Hours — Should you give AI agents access to API keys and private credentials, and if so, what isolation strategies actually work?

Office Hours — What's the best way to set cost limits and prevent AI agents from burning through your API budget on failed or inefficient tasks?

Paper of the Week — Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use

Office Hours — How do you prevent AI agents from accidentally introducing vulnerable dependencies or malicious code when they autonomously modify your codebase?

Office Hours — What's your strategy for testing and evaluating LLM outputs in production now that Promptfoo was acquired?

Office Hours — Has anyone successfully fine-tuned LLMs for production use and what was the ROI?

Office Hours — Is operational memory a missing layer in AI agent architecture?

Office Hours — What hiring criteria should you use when your team is heavily using AI-assisted coding?

Office Hours — Who is actually getting measurable value from AI agents in production?

Paper of the Week — Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations

Office Hours — How do you know if AI agents will choose your tool?

Office Hours — Has anyone deployed LLMs to production and what were the biggest operational challenges?

Office Hours — How are you extracting the best performance out of your RAG pipeline?

Office Hours — Is anyone using function calling with LLMs in production?

Office Hours — Best LLM stack for Q&A over internal PDFs?

Office Hours — What tools are you using for AI evals, and why does everything feel half-baked?

Paper of the Week — Lossless Prompt Compression via Dictionary-Encoding and In-Context Learning: Enabling Cost-Effective LLM Analysis of Repetitive Data

Office Hours — Is synthetic data generation practical outside academia?

Office Hours — Are we pretending RAG is ready, when it's barely out of demo phase?

Office Hours — Why don't we have a functional DSL for data+embedding+API pipelines?

Office Hours — Why are so many companies rolling out their own AI/LLM agent sandboxing solution?

Office Hours — What are some actual use cases of AI Agents right now?

Office Hours — Can you personalize an LLM by fine-tuning it on conversation history, or is that the wrong approach?

Office Hours — Who is actually getting real business value from AI agents right now, and how?

Paper of the Week — Mamba-Based State Space Models for Long-Context Retrieval-Augmented Generation

Office Hours — Is fine-tuning LLMs actually worth it, or is prompt engineering and RAG always the better path?

Office Hours — Is fine-tuning the right approach when you want a model to reliably remember your codebase or company-specific data?

Office Hours — What kinds of tasks are AI agents actually reliable at in production today?

Office Hours — Is RAG an antipattern for AI agents, or is it still the right default approach?

Office Hours — How do you actually monitor AI agents in production when there's no standard playbook yet?

Office Hours — We're using an LLM to extract structured data from messy PDFs. Sometimes it works perfectly, sometimes it misses fields or invents data. How do I know if the problem is the model, my prompt, or the PDF quality itself?

Office Hours — We're getting inconsistent outputs from the same prompt with GPT-5.4. Temperature is locked at 0. What's actually going on?

Paper of the Week — SnapKV: LLM Knows What You are Looking for Before Generation

Office Hours — I'm using Claude Opus 4.6 for a customer-facing summarization task. Should I batch requests during off-peak hours to save money, or just call the API in real-time?

Office Hours — How do I know when to stop prompt engineering and just upgrade my model?

Office Hours — Is it better to improve the harness around the LLM or wait for a better model?

Office Hours — Should I A/B test my LLM prompts in production or is that overkill?

Office Hours — What's the hardest part of building AI agents that actually work?

Office Hours — How do you actually test LLM apps beyond vibe checks?

Office Hours — Why is AI agent reliability barely improving despite 18 months of model upgrades?

Paper of the Week — Training Language Models to Self-Correct via Reinforcement Learning

Office Hours — How are people safely reusing cached LLM answers in production RAG systems?

Office Hours — Do structured outputs from LLMs create false confidence that the response is actually correct?

Office Hours — How are you handling LLM API costs in production without sacrificing quality?

Office Hours — How do I actually know if my LLM is hallucinating in production?