Improve LLM Performance by Maximizing Iteration Speed
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…
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In the maker’s words, at launch
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 different permutations, measure performance in an objective way, and compare performance across each other to find the best possible combination. --- I have been working in AI since 2021 - first at FAANG with ML, then with LLM in start-ups since early 2023. I have had the chance to talk with many companies working with AI. The biggest mistake I see is a lack of standard process that allows them to rapidly iterate towards their performance goal. Using my learnings, I’m working on an OSS framework that structures your LLM Application Development for Rapid Iteration so you can reach your performance targets much faster. - If you are interested, you can learn more about it at: https://palico.ai/ - It's also Open Source and you can get setup with a single command. Stars are always appreciated. You can checkout the repo at: https://github.com/palico-ai/palico-main
Does the same job
all alternatives →- SBStandardizing 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…
- HPHorizon – Programmatic Prompt Generation and LLM Configurations2023 · gethorizon.ai · ▲7
Hi HN. I heard you like dev tools and AI, so we wanted to share our project that we’ve been working on. We’re working on Horizon [1] - a higher level abstraction for LLMs so that developers can spend less time trying to grapple with LLMs to make them work and more time with users. This is the starting feature set which takes an auto-ML approach to identify the optimal LLM model, hyperparameters, and prompt - instead of just giving you the tooling to figure it out yourself. You can read more about it in our documentations. Our view is that as LLMs become increasingly commoditized and prompts…
- PRPrompt-refiner – Lightweight optimization for LLM inputs and RAGDec 2025 · github.com · ▲7
Hi HN, While building RAG agents, I noticed a lot of token budget was wasted on formatting overhead (HTML tags, JSON structure, whitespace). Existing solutions felt too heavy (often requiring torch/transformers), so I wrote this lightweight, zero-dependency library to solve it. It includes strategies for context packing, PII redaction, and tool output compression. Benchmarks show it can save ~15% of tokens with negligible latency overhead (<0.5ms). Happy to answer any questions!
- CRChainFactory – Run Structured LLM Inference with Easy Parallelism2024 · github.com · ▲8
hi everyone. how does moving llm call prompts and output structure definitions away from code into configuration land sound? would you use something like this if it was stable and well documented enough? please don't hold back the criticism. i appreciate all feedback (constructive & otherwise).
- LBLlm-benchmark – Benchmarks LLM-optimized code across multiple providers2025 · github.com · ▲8
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