Using LLMs to Bridge Tech Execution Gaps

LLM models make a TON of mistakes, but with 1. good documentation, 2. good code review, 3. the best models available, you can flawlessly accomplish very large changes, FASTER and BETTER than a human. Here’s a real example. At Formation, we have Session Studio: our live session environment. It’s a real-time system with video, audio, chat, reactions, slides, hand-raising, polls, collaborative coding pads… the works. We recently changed the definitions of participant roles. It was a deep permission and behavior refactor across a complex, real-time surface area with dozens of flags and conditional checks. The kind of change that’s easy to partially ship and quietly break production. Here’s how I used AI to pull it off: 1. Full System Audit: Codex generated a ~1,300-line audit of the entire current state, every permission path, flag, edge case, and role interaction. 2. Proposed Redesign: Codex then wrote a second document detailing every change required to support the new role definitions. 3. Engineering Plan: Using "plan mode" first, Claude merged both documents into a structured engineering spec with clear implementation phases. 4. "Adversarial" Iteration: Claude and Codex iterated on the docs, flagging inconsistencies, ambiguities, and decisions that required human judgment. I acted as editor-in-chief, resolving tradeoffs and clarifying intent. 5. Phased Execution (8 Phases). For each phase: Claude implemented, Codex reviewed, Claude fixed... Repeat until clean, then Final Claude review. Total time: ~24 hours of async back-and-forth. The key insight: LLMs are unreliable in isolation. They’re extremely powerful inside a system of documentation, review, and phased execution.

We know LLMs can substantially improve developer productivity. But the outcomes are not consistent. An extensive research review uncovers specific lessons on how best to use LLMs to amplify developer outcomes. 💡 Leverage LLMs for Improved Productivity. LLMs enable programmers to accomplish tasks faster, with studies reporting up to a 30% reduction in task completion times for routine coding activities. In one study, users completed 20% more tasks using LLM assistance compared to manual coding alone. However, these gains vary based on task complexity and user expertise; for complex tasks, time spent understanding LLM responses can offset productivity improvements. Tailored training can help users maximize these advantages. 🧠 Encourage Prompt Experimentation for Better Outputs. LLMs respond variably to phrasing and context, with studies showing that elaborated prompts led to 50% higher response accuracy compared to single-shot queries. For instance, users who refined prompts by breaking tasks into subtasks achieved superior outputs in 68% of cases. Organizations can build libraries of optimized prompts to standardize and enhance LLM usage across teams. 🔍 Balance LLM Use with Manual Effort. A hybrid approach—blending LLM responses with manual coding—was shown to improve solution quality in 75% of observed cases. For example, users often relied on LLMs to handle repetitive debugging tasks while manually reviewing complex algorithmic code. This strategy not only reduces cognitive load but also helps maintain the accuracy and reliability of final outputs. 📊 Tailor Metrics to Evaluate Human-AI Synergy. Metrics such as task completion rates, error counts, and code review times reveal the tangible impacts of LLMs. Studies found that LLM-assisted teams completed 25% more projects with 40% fewer errors compared to traditional methods. Pre- and post-test evaluations of users' learning showed a 30% improvement in conceptual understanding when LLMs were used effectively, highlighting the need for consistent performance benchmarking. 🚧 Mitigate Risks in LLM Use for Security. LLMs can inadvertently generate insecure code, with 20% of outputs in one study containing vulnerabilities like unchecked user inputs. However, when paired with automated code review tools, error rates dropped by 35%. To reduce risks, developers should combine LLMs with rigorous testing protocols and ensure their prompts explicitly address security considerations. 💡 Rethink Learning with LLMs. While LLMs improved learning outcomes in tasks requiring code comprehension by 32%, they sometimes hindered manual coding skill development, as seen in studies where post-LLM groups performed worse in syntax-based assessments. Educators can mitigate this by integrating LLMs into assignments that focus on problem-solving while requiring manual coding for foundational skills, ensuring balanced learning trajectories. Link to paper in comments.

Focusing on AI’s hype might cost your company millions… (Here’s what you’re overlooking) Every week, new AI tools grab attention—whether it’s copilot assistants or image generators. While helpful, these often overshadow the true economic driver for most companies: AI automation. AI automation uses LLM-powered solutions to handle tedious, knowledge-rich back-office tasks that drain resources. It may not be as eye-catching as image or video generation, but it’s where real enterprise value will be created in the near term. Consider ChatGPT: at its core, there is a large language model (LLM) like GPT-3 or GPT-4, designed to be a helpful assistant. However, these same models can be fine-tuned to perform a variety of tasks, from translating text to routing emails, extracting data, and more. The key is their versatility. By leveraging custom LLMs for complex automations, you unlock possibilities that weren’t possible before. Tasks like looking up information, routing data, extracting insights, and answering basic questions can all be automated using LLMs, freeing up employees and generating ROI on your GenAI investment. Starting with internal process automation is a smart way to build AI capabilities, resolve issues, and track ROI before external deployment. As infrastructure becomes easier to manage and costs decrease, the potential for AI automation continues to grow. For business leaders, identifying bottlenecks that are tedious for employees and prone to errors is the first step. Then, apply LLMs and AI solutions to streamline these operations. Remember, LLMs go beyond text—they can be used in voice, image recognition, and more. For example, Ushur is using LLMs to extract information from medical documents and feed it into backend systems efficiently—a task that was historically difficult for traditional AI systems. (Link in comments) In closing, while flashy AI demos capture attention, real productivity gains come from automating tedious tasks. This is a straightforward way to see returns on your GenAI investment and justify it to your executive team.

Most AI code isn’t broken. It’s just broken enough to break you. LLMs sound confident. They move fast. Their code looks perfect… until it runs. Then come the silent bugs and missed edge cases. Here are 8 principles from Simon Willison that stop the bugs before they stop your team: 🔸 LLMs are junior developers, not autonomous agents ↳ They need structure, supervision, and review. You wouldn’t ship a junior’s code without checking it. Don’t ship an LLM’s code without testing it thoroughly. 🔸 Context quality determines output quality ↳ The difference between usable and unusable code often comes down to context. Include requirements, constraints, edge cases, and error handling needs. Specificity here prevents hours of debugging later. 🔸 Knowledge cutoffs matter ↳ GPT-4 was trained up to October 2023. Claude 3.5 up to April 2024. LLMs won’t know the latest changes to libraries or APIs so verify against current docs every time. 🔸 Use iterative refinement ↳ Start with a broad prompt: “What are my implementation options?” Then narrow it: “Implement option 2 using these parameters.” Then polish: “Add robust error handling and tests.” This mirrors how senior developers already think. 🔸 Test every generated line ↳ LLMs are confident, even when wrong. They excel at writing syntactically correct code with subtle logical flaws. Assume nothing works until it's tested. 🔸 Leverage safe execution environments ↳ Tools like Claude Artifacts and ChatGPT Code Interpreter let you run code in a sandbox. Validate before you deploy. This step prevents production incidents. 🔸 Embrace ‘vibe-coding’ for discovery ↳ Use vibe-coding to test ideas, experiment, and learn system boundaries. That experimentation leads to sharper production use. 🔸 LLMs amplify existing expertise ↳ They make experienced developers faster. They don’t replace core understanding. If you’re not leveling up alongside your tools, you’re falling behind. The engineers getting the most out of AI aren’t asking it to code. They’re treating it like a teammate with limits. What’s your most effective LLM workflow? ♻️ Repost to help your team use AI more strategically ➕ Follow me, Sairam, for practical AI engineering insights

Could LLMs handle large-scale code migrations at Google? Yep, Google actually did this, at massive scale, over real production code, no simulation or academic experiment, it was a live, enterprise-grade system. This represents the most comprehensive real-world code migration using LLMs publicly documented so far, and was largely successful with Developers estimating a 50% reduction in total migration time compared to previous manual-only efforts. LLMs successfully automated the majority of code edits and changes and were used to modify production code and tests, handle edits across multiple programming languages (Java, C++, etc.), and apply domain-specific transformations (e.g., in SQL strings). LLM generated changes were passed through six validation stages, including parsing, compilation, and test pass checks. Developers loved it, and expressed high satisfaction with the LLM edits, and found it especially helpful for repetitive or tedious edits. They reported a strong sense of progress due to nightly automation updates. Obviously there were still significant challenges: - LLM hallucinations (reformatting instead of migrating). - Context window limitations - large files sometimes couldn’t be processed. - Language support gaps - Dart had lower LLM success than Java/C++. - Pre-existing test failures or golden files blocked some automated rollouts. - LLMs required retries in some cases. Still, quite astonishing when you consider where we were just a couple of years ago.

Building AI is exciting, but running it in production is humbling. Resist the urge to solve everything with an LLM. The best AI systems combine language models with traditional engineering approaches. After years of deploying LLMs in production environments, here are the critical lessons that made the difference between demo-ready and production-ready AI: 1. The gap between prototype and production is massive What works in a demo may fall apart in the real world. Plan for more time, more edge cases, and more iterations than you think 2. LLMs are confident liars (they can lie with a straight face) Hallucinations persist regardless of your model size. Always implement robust guardrails in place: validation logic, retrieval augmentation, or human feedback 3. Prioritize data quality over parameter count Smaller, well-trained models outperform larger ones with poor data. Superior models can be built with fewer parameters if you prioritise high-quality, diverse training data 4. Latency matters more than you expect Slow responses drive user drop-off. Optimise token usage, use caching, and hybrid retrieval techniques to mitigate delays. 5. Prompt engineering is like software engineering Prompts should be versioned, tested, and logged like any other critical code. Small prompt changes can lead to wildly different behaviours, good and bad. 6. Cost needs to be managed intentionally LLMs are not cheap. Prompt compression, result caching, implementing parameter-efficient methods, and choosing the right infrastructure can drastically reduce production spend without degrading quality. 7. Monitoring is non-negotiable Track not just traditional metrics but also prompt quality, hallucination rates, and semantic accuracy. You can't improve what you don't measure. 8. Security and data privacy aren't optional LLMs introduce unique security challenges that traditional approaches can't address. Users expect both intelligence and safety. Implement multi-layered defences with input sanitisation, prompt injection protection, and regular red-teaming. Anonymise inputs, maintain transparent data flows, and prevent sensitive data leakage through logs. 9. Fine-tuning isn't always the answer Sometimes, better prompting or RAG architecture improvements yield better results than expensive fine-tuning efforts. These lessons weren't only learned from reading papers, but they came from late nights debugging, customer escalations, and hard-earned production wins. As our industry races to deploy ever more capable models, the gap between research and reliable systems remains significant. The teams that will succeed are those who balance AI innovation with engineering skills. What lessons would you add from your own deployment experiences? I'd love to continue learning together. Follow me for more Anthony Soronnadi #ai #llm #machinelearning #deeplearning #llmops #mlops #mcp

Most enterprise AI programmes stall at the same place: the gap between a model demo and a platform that autonomous business lines actually trust. My team ships across that gap daily. Today, we published new research — LLMs continuously translating and extending their own production infrastructure, with public benchmarks as the objective function. 648K lines of Rust → 41K of Python. Near-parity on real-world task accuracy, then beyond parity: 30 enterprise capabilities shipped behind feature flags without breaking the live baseline. https://lnkd.in/eByS9M7b I care about Rust memory models on Tuesday and P&L impact on Wednesday. The rare part isn't either skill — it's refusing to let go of one to hold the other. Nobody asked us to do this. No roadmap item, no OKR. We did it because the problem was there, and we wanted to know. The best engineers I've worked with don't need permission to be curious — they need air cover. That's my job. Biswa Sengupta PhD Jinhua Wang JPMorganChase #LLMSuite

Many AI startups seem to be building their products on a hope and a prayer! They have surrendered control of core workflows in their businesses to 3rd-party LLM APIs. These startups communicate with these LLMs via special incantations that are expressed in arcane structures (called prompts but more like pleas), cajoling, negotiating, hoping that the LLMs would grant their wishes. Prompt Pundits on social media and blogs argue about the best way to encode the needs of the business into these entreaties, just so that the LLMs would bless them with consistent results. They offer their services to these startups as prompt engineers/consultants, but they aren't able to control the whims of LLMs either (let alone updates, retraining schedules of LLM providers). LLMs operate in mysterious ways, indeed! I'm exaggerating a bit here, but not by much. Imagine running a tech startup on a cloud provider that executes 75% of the code, and a different 75% each time. No Eng Leader would ever allow this; but many seem to be ok with this type of execution substrate for LLM-powered workflows. Don't get me wrong. I'm a big fan of LLMs; they have enabled me to build AI features at Bluebirds with an accuracy and iteration velocity that I could only dream of during my last startup. So, if you want to have more control over your LLM-powered workflows, here are some recommendations. 1. LLMs are best at extraction from unstructured text and generation of natural language. As much as possible restrict calls to LLMs to these steps. 2. As a Product Leader you know the job-to-be-done for your users best; if you don't, this is the what you should spend time on. You cannot rely on LLMs to fill this gap! 3. Break down the workflows into as granular steps as possible, and enlist LLMs for the parts they are best at. (see pt 1) 4. If you must use LLMs for reasoning across multiple steps, then try providing the LLM with the explicit flows/logic. For some domains (like Sales) there is a lot of content online that LLMs have been exposed to. This helps them create generic plans ("chain of thought!"), which is sufficient if you wanted to offer your users as much insight as a click-baity sales blog post. But if you have unique insights into the workflow (see pt 2) then break it down into smaller steps and use LLMs strategically for them. This is your moat, after all! This is the path we have taken at Bluebirds when designing our Agentic systems. Curious to hear if others have more or different recommendations.

Tips for AI-Assisted software development: Use AI for more than just coding Most software engineers treat AI like a code generator. That leaves a lot of value on the table. LLMs can help across the entire software development lifecycle, from shaping a problem to shipping and maintaining the solution. You use AI as a sounding board that questions your assumptions, pokes holes in your logic, and helps you sharpen your ideas before anyone writes a line of code. Here are some practical ways to put AI to work outside the editor: Planning: turn messy inputs into user stories and acceptance criteria, spot gaps in requirements, and ask the model to challenge your assumptions. Design: draft architecture docs, generate API specs, explore alternatives, and have the model pressure-test your design choices. Development: generate documentation, test data, migrations, and cross-format conversions while asking the model to highlight edge cases you missed. Testing: propose test scenarios, surface tricky boundaries, analyze logs, and ask the model to explain failures in plain language. DevOps: write CI/CD configs, create IaC templates, and have the model critique your deployment strategy. Maintenance: summarize long threads, explain legacy code, highlight risky areas, and suggest low-effort improvements. Communication: write stakeholder updates, outline blog posts, prepare presentations, and draft questions you should be asking but aren’t. Actionable step: pick a real piece of work you’re doing this week. Ask an LLM to challenge it. Tell it to look for gaps, risks, and blind spots. Use that review to refine your thinking before you move on to execution.

Recent research is advancing two critical areas in AI: autonomy and reasoning, building on their strengths to make them more autonomous and adaptable for real-world applications. Here is a summary of a few papers that I found interesting and rather transformative: • 𝐋𝐋𝐌-𝐁𝐫𝐚𝐢𝐧𝐞𝐝 𝐆𝐔𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 (𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭): These agents use LLMs to interact directly with graphical interfaces—screenshots, widget trees, and user inputs—bypassing the need for APIs or scripts. They can execute multi-step workflows through natural language, automating tasks across web, mobile, and desktop platforms. • 𝐀𝐅𝐋𝐎𝐖: By treating workflows as code-represented graphs, AFLOW dynamically optimizes processes using modular operators like “generate” and “review/revise.” This framework demonstrates how smaller, specialized models can rival larger, general-purpose systems, making automation more accessible and cost-efficient for businesses of all sizes. • 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 (𝐑𝐀𝐑𝐄): RARE integrates real-time knowledge retrieval with logical reasoning steps, enabling LLMs to adapt dynamically to fact-intensive tasks. This is critical in fields like healthcare and legal workflows, where accurate and up-to-date information is essential for decision-making. • 𝐇𝐢𝐀𝐑-𝐈𝐂𝐋:: Leveraging Monte Carlo Tree Search (MCTS), this framework teaches LLMs to navigate abstract decision trees, allowing them to reason flexibly beyond linear steps. It excels in solving multi-step, structured problems like mathematical reasoning, achieving state-of-the-art results on challenging benchmarks. By removing the reliance on APIs and scripts, systems like GUI agents and AFLOW make automation far more flexible and scalable. Businesses can now automate across fragmented ecosystems, reducing development cycles and empowering non-technical users to design and execute workflows. Simultaneously, reasoning frameworks like RARE and HiAR-ICL enable LLMs to adapt to new information and solve open-ended problems, particularly in high-stakes domains like healthcare and law. These studies highlight key emerging trends in AI: 1. APIs and Simplifying Integration: A major trend is the move away from API dependencies, with AI systems integrating directly into existing software environments through natural language and GUI interaction. This addresses one of the largest barriers to AI adoption in organizations. 2. Redefining User Interfaces: Traditional app interfaces with icons and menus are being reimagined. With conversational AI, users can simply ask for what they need, and the system executes it autonomously. 3. Tackling More Complex Tasks Autonomously: As reasoning capabilities improve, AI systems are expanding their range of activities and elevating their ability to plan and adapt. As these trends unfold, we’re witnessing the beginning of a new era in AI. Where do you see the next big research trends in AI heading?