Improve LLM Performance by Maximizing Iterative Development
I have been working in AI space for a while now, first at FAANG with ML since 2021, then with LLM in start-ups since early 2023. I think LLM Application development is extremely iterative, more so than any other types of development. This is because to improve an LLM application performance (accuracy, hallucinations, latency, cost), you need to try various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt context with different RAG architecture, different agent architecture, and more. There are thousands of possible combinations and you need a process…
Improve LLM Performance by Maximizing Iterative Development launched on July 3, 2024 with 104 votes, #731 of 5,184 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
This tool helps developers systematically test and evaluate different combinations of language models, prompts, and retrieval architectures for LLM applications. It addresses the iterative nature of AI development by providing a structured process to quickly experiment with variables like model selection, prompt templates, RAG configurations, and agent designs. The tool is designed for teams building LLM products who need to optimize for accuracy, reduce hallucinations, and improve latency and cost without manually managing thousands of possible combinations.
written from the facts on this page · September 2026
From the sources
Build, Improve Performance, and Productionize your AI Application - palico-ai/palico-ai
In the maker’s words, at launch
I have been working in AI space for a while now, first at FAANG with ML since 2021, then with LLM in start-ups since early 2023. I think LLM Application development is extremely iterative, more so than any other types of development. This is because to improve an LLM application performance (accuracy, hallucinations…
Does a similar job
all alternatives →- ILImprove LLM Performance by Maximizing Iteration Speed2024 · palico.ai · ▲5
LLM Application development is extremely iterative, more so than any other types of development. This is because in addition to all the activities involved in regular application development, we also need to make the LLM Application accurate and reduce hallucination. To improve performance, we need to trial and error various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt context with different RAG architecture, try different agent architecture, and more. There are thousands of permutations to try. We need to be able to easily experiment with these…
Standardizing build, experiment, and deployment for LLM Development2024 · github.com · ▲6*Motivation* Hi hackers, I'm Asif. I know we dislike premature standardization, but hear me out. LLM Application development is extremely iterative, more so than most other types of application development. We need a process that allows us to iterate faster. LLM Development is highly iterative due to the activities that come with regular software development, as well as the need to make the LLM Application accurate and reduce hallucination. To improve hallucination, we need to trial and error various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt…
Why write code if the LLM can just do the thing? (web app experiment)Nov 2025 · github.com · ▲436I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
Faster LLM evaluation with Bayesian optimization2024 · github.com · ▲131Recently I've been working on making LLM evaluations fast by using bayesian optimization to select a sensible subset. Bayesian optimization is used because it’s good for exploration / exploitation of expensive black box (paraphrase, LLM). I would love to hear your thoughts and suggestions on this!
Speeding up LLM inference 2x times (possibly)2024 · asciinema.org · ▲419Here's a project I've been working on for the last few months. It's a new (I think) algorithm, that allows to adjust smoothly - and in real time - how many calculations you'd like to do during inference of an LLM model. It seems that it's possible to do just 20-25% of weight multiplications instead of all of them, and still get good inference results. I implemented it to run on M1/M2/M3 GPU. The mmul approximation itself can be pushed to run 2x fast before the quality of output collapses. The inference speed is just a bit faster than Llama.cpp's, because the rest of implementation…
TensorZero2025 · github.com · ▲307Hi HN! We're Gabriel & Viraj, and we're excited to open source TensorZero. To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. 1. Integrate our model gateway 2. Send metrics or feedback 3. Unlock compounding improvements in quality, cost, and latency It enables a data & learning flywheel for LLMs by unifying: • Inference: one API for all LLMs, with <1ms P99 overhead • Observability: inference & feedback → your database • Optimization: better prompts, models, inference strategies • Experimentation:…
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