Close Menu
    Trending
    • Optimizing Data Transfer in Distributed AI/ML Training Workloads
    • Achieving 5x Agentic Coding Performance with Few-Shot Prompting
    • Why the Sophistication of Your Prompt Correlates Almost Perfectly with the Sophistication of the Response, as Research by Anthropic Found
    • From Transactions to Trends: Predict When a Customer Is About to Stop Buying
    • America’s coming war over AI regulation
    • “Dr. Google” had its issues. Can ChatGPT Health do better?
    • Evaluating Multi-Step LLM-Generated Content: Why Customer Journeys Require Structural Metrics
    • Why SaaS Product Management Is the Best Domain for Data-Driven Professionals in 2026
    ProfitlyAI
    • Home
    • Latest News
    • AI Technology
    • Latest AI Innovations
    • AI Tools & Technologies
    • Artificial Intelligence
    ProfitlyAI
    Home » Achieving 5x Agentic Coding Performance with Few-Shot Prompting
    Artificial Intelligence

    Achieving 5x Agentic Coding Performance with Few-Shot Prompting

    ProfitlyAIBy ProfitlyAIJanuary 23, 2026No Comments9 Mins Read
    Share Facebook Twitter Pinterest LinkedIn Tumblr Reddit Telegram Email
    Share
    Facebook Twitter LinkedIn Pinterest Email


    helpful instruments, particularly for programmers. I actually use LLMs each single day, and may’t think about a world with out them. Nevertheless, there are a couple of explicit strategies you possibly can make the most of to attain even larger outcomes with LLMs.

    I’ve coated a couple of completely different strategies in earlier articles, resembling:

    • Utilizing Slash instructions
    • Using plan mode
    • Constantly updating brokers.md

    On this article, I’ll cowl how one can leverage few-shot prompting to have your LLMs carry out even higher.

    Why use few-shot prompting

    Firstly, I need to cowl why you need to make the most of few-shot prompting. Few-shot prompting is extremely helpful as a result of it permits you to present the LLM your intent with out having to explicitly write the intent out in your immediate.

    For instance, let’s say you desire a web site achieved in a specific manner, just like a earlier web site you made. And with out few-shot prompting, you can attempt to describe the earlier web site you need replicated and have the LLM create that new web site. Nevertheless, this can probably result in a variety of ambiguity in your immediate, the place the LLM has to make some assumptions. Thus, you’ll probably not obtain the outcome you’re in search of.

    If as an alternative you present the LLM with the precise codebase, or at the very least some screenshots of your earlier web site, and easily ask it to duplicate the web site, you’ll obtain significantly better outcomes. This basically removes all ambiguity out of your immediate and helps the LLM obtain a lot larger outcomes.

    I’m arguing for the truth that you need to use this few-shot prompting approach in every little thing you do. So long as it’s not the primary time you’re engaged on a activity, all the time discuss with a few of your earlier work for a way the LLM ought to do one thing. For instance:

    • Making advertising and marketing materials? -> present the LLM your earlier work
    • Including a brand new characteristic to your app? -> present the LLM your earlier options
    • Creating new slash instructions? -> present the LLM the way you structured your earlier slash instructions

    I virtually assure you that by referring to your earlier work and displaying the LLM tips on how to do one thing not solely within the immediate, however in precise implementation, you’ll obtain a lot larger outcomes.

    This infographic highlights the primary contents of this text. I’ll talk about few-shot prompting and the way you leverage it to optimize your LLM’s efficiency. I’ll cowl factors like: organizing your previous work, tips on how to present few-shot examples, iterating in your work, and the way increasing your library of labor will additional enhance your LLM’s efficiency. Picture by Gemini

    The way to implement few-shot prompting

    Now I need to talk about tips on how to implement few-shot prompting. Few-shot prompting will not be one thing you possibly can all the time implement. Some duties are merely new, and it’s very laborious to benefit from or leverage earlier work that you just’ve achieved as a result of the brand new work merely isn’t related sufficient.

    That is utterly fantastic and one thing you need to settle for. Nevertheless, you need to all the time search for alternatives to leverage few-shot prompting. Firstly, I’ll talk about how you need to set up your work to extend the floor space for few-shot prompting alternatives, and I’ll then present you tips on how to do few-shot prompting in follow, utilizing examples.

    Organizing your work

    Firstly, it’s vital that you just set up all of your work in accessible folders in your laptop. Personally, I retailer virtually every little thing I do inside a programming major folder. I then have a folder construction of the code repositories I’m usually working in. One other folder consisting of some private initiatives I’m accessing generally. One other folder with the advertising and marketing materials I’m engaged on, resembling LinkedIn posts and short-form movies, and one other folder for all the shows I’m holding on AI.

    Now, at any time when I begin a brand new activity, my first job is all the time to determine which folder this work belongs to. On the whole, organizing work like that is simply common laptop organizing hygiene. Nevertheless, being organized like this makes it a lot easier to benefit from few-shot prompting sooner or later. I simply all the time suggest spending a while determining the place your work belongs at first to be able to benefit from it on a later event.

    Moreover, you need to all the time be committing your work to GitHub. The explanation for that is that it permits you to retailer all of your progress and offers you with a model historical past. So if one thing occurs to your laptop, otherwise you make adjustments you need to revert, you possibly can simply revert them utilizing Git.

    Moreover, in the event you don’t have data of utilizing Git, it’s probably not a difficulty, as you possibly can merely use an LLM to work together with Git for you. You don’t actually must work together with Git in any respect your self.

    Few-shot prompting in motion

    Now, assuming you’ve organized your work correctly, it’s time to start out making the most of few-shot prompting. The idea of few-shot prompting is fairly easy. Everytime you begin new work, you merely discuss with a folder or file of earlier work that you really want the pc to both replicate or observe the identical styling or related.

    I believe it’s best if I present you, if I describe some particular examples of how I take advantage of few-shot prompting in follow.

    Writing code

    In all probability the most typical use case for me when few-shot prompting is writing code. Let’s say I need to implement a GitHub Actions validation script in a brand new repository. I basically by no means ask Claude Code to give you this script from scratch. As an alternative, I merely inform Claude Code, “This script exists in folder X, replicate or duplicate the script precisely within the repository I’m presently engaged on. Nevertheless, simply make this one change the place you don’t run the a part of the validation script”.

    This has two major advantages. For one, I’m virtually sure I’ll get the GitHub Actions validation script I’m anticipating, as a result of I do know it’s working within the different repository. Moreover that is nice as a result of despite the fact that I’m copying over the script from one other repository, I’m nonetheless capable of make adjustments. And on this instance, the change was that I don’t need to run the total validation script. I need to skip one a part of it on this new repository.

    Claude Code is nice at coping with these sorts of duties, the place you inform it to duplicate another piece of code after which make a couple of personalized adjustments. Which is why this works so nicely.

    Creating advertising and marketing materials

    One other quite common use case I’ve for few-shot prompting is creating advertising and marketing materials. Creating recent advertising and marketing materials generally is a time-consuming activity. You need to, for instance, create model new shows or carousel views for use on LinkedIn.

    Moreover, it’s typically laborious to explain your actual preferences in relation to shows. You may want a specific form of font type or a specific form of alignment of textual content and pictures in your shows. That is merely laborious to explain in pure language, but it surely’s very clear to the mannequin in the event you present it an instance of how this textual content font is or how textual content and pictures are aligned out of your earlier work.

    Thus, once I’m making a brand new presentation these days, I all the time present Claude Code my earlier shows and inform it the issues I need to change from these earlier shows. The issues I need to change are usually the precise content material of the presentation, in fact, the place I describe every web page in my presentation to as a lot element as doable. That is, in fact, vital to maintain the content material yours and never AI-generated.

    Moreover, I merely iterate rather a lot with Claude Code. I advised it to make me an preliminary draft of the presentation. I then overview the draft, transcribe all the adjustments I need modified via MacWhisper whereas reviewing the presentation, and have the AI make a second draft. I’ll then proceed like this till I’m pleased with the presentation.

    Slash instructions

    Creating slash instructions can be one thing I do on a reasonably common foundation. Slash instructions are basically saved prompts that you may have with the code that permits you to entry prompts quickly. I usually have slash instructions for instructions like making a pull request to dev, making a pull request to major, simplifying code, or working a PR overview.

    Nevertheless, I usually need my slash instructions to observe a specific form of construction. The construction is a markdown construction with a couple of factors that I usually share throughout my completely different slash instructions. Thus, displaying Claude Code my earlier slash instructions makes the era of recent slash instructions rather a lot easier, quicker, and extra prone to observe the preferences I’ve.

    Conclusion

    On this article, I’ve mentioned tips on how to leverage few-shot prompting to attain the most effective outcomes along with your LLMs. Energetic utilization of few-shot prompting by displaying the LLM examples of your earlier work could make your LLM way more environment friendly in your use circumstances. I like to recommend all the time striving to make use of few-shot prompting everytime you work with LLMs to attain the most effective outcomes. The most effective a part of few-shot prompting is that it will get higher the extra work you do. The extra work you do, the extra earlier examples it’s important to present the LLM, and the higher it can carry out in accordance with your preferences, which is what makes it such an amazing approach.

    👉 My free eBook and Webinar:

    🚀 10x Your Engineering with LLMs (Free 3-Day Email Course)

    📚 Get my free Vision Language Models ebook

    💻 My webinar on Vision Language Models

    👉 Discover me on socials:

    💌 Substack

    🔗 LinkedIn

    🐦 X / Twitter



    Source link

    Share. Facebook Twitter Pinterest LinkedIn Tumblr Email
    Previous ArticleWhy the Sophistication of Your Prompt Correlates Almost Perfectly with the Sophistication of the Response, as Research by Anthropic Found
    Next Article Optimizing Data Transfer in Distributed AI/ML Training Workloads
    ProfitlyAI
    • Website

    Related Posts

    Artificial Intelligence

    Optimizing Data Transfer in Distributed AI/ML Training Workloads

    January 23, 2026
    Artificial Intelligence

    Why the Sophistication of Your Prompt Correlates Almost Perfectly with the Sophistication of the Response, as Research by Anthropic Found

    January 23, 2026
    Artificial Intelligence

    From Transactions to Trends: Predict When a Customer Is About to Stop Buying

    January 23, 2026
    Add A Comment
    Leave A Reply Cancel Reply

    Top Posts

    A Guide for Enterprise Leaders

    September 5, 2025

    Features, Benefits, Review and Alternatives • AI Parabellum

    June 27, 2025

    AI Might Take Your Job. But These Roles Could Be Your Future

    June 24, 2025

    How to Make AI Your Smartest Business Strategist with Jen Taylor [MAICON 2025 Speaker Series]

    October 2, 2025

    Nano Banana kommer till Google Sök, NotebookLM och Foton

    October 15, 2025
    Categories
    • AI Technology
    • AI Tools & Technologies
    • Artificial Intelligence
    • Latest AI Innovations
    • Latest News
    Most Popular

    TruthScan vs. Grammarly: Which AI Detector Works Best?

    December 3, 2025

    Researchers teach LLMs to solve complex planning challenges | MIT News

    April 4, 2025

    Designing a new way to optimize complex coordinated systems | MIT News

    April 25, 2025
    Our Picks

    Optimizing Data Transfer in Distributed AI/ML Training Workloads

    January 23, 2026

    Achieving 5x Agentic Coding Performance with Few-Shot Prompting

    January 23, 2026

    Why the Sophistication of Your Prompt Correlates Almost Perfectly with the Sophistication of the Response, as Research by Anthropic Found

    January 23, 2026
    Categories
    • AI Technology
    • AI Tools & Technologies
    • Artificial Intelligence
    • Latest AI Innovations
    • Latest News
    • Privacy Policy
    • Disclaimer
    • Terms and Conditions
    • About us
    • Contact us
    Copyright © 2025 ProfitlyAI All Rights Reserved.

    Type above and press Enter to search. Press Esc to cancel.