LLM Applications for Intermediate Programming Tasks

If you’re building with LLMs, these are 10 toolkits I highly recommend getting familiar with 👇 Whether you’re an engineer, researcher, PM, or infra lead, these tools are shaping how GenAI systems get built, debugged, fine-tuned, and scaled today. They form the core of production-grade AI, across RAG, agents, multimodal, evaluation, and more. → AI-Native IDEs (Cursor, JetBrains Junie, Copilot X) Modern IDEs now embed LLMs to accelerate coding, testing, and debugging. They go beyond autocomplete, understanding repo structure, generating unit tests, and optimizing workflows. → Multi-Agent Frameworks (CrewAI, AutoGen, LangGraph) Useful when one model isn’t enough. These frameworks let you build role-based agents (e.g. planner, retriever, coder) that collaborate and coordinate across complex tasks. → Inference Engines (Fireworks AI, vLLM, TGI) Designed for high-throughput, low-latency LLM serving. They handle open models, fine-tuned variants, and multimodal inputs, essential for scaling to production. → Data Frameworks for RAG (LlamaIndex, Haystack, RAGflow) Builds the bridge between your data and the LLM. These frameworks handle parsing, chunking, retrieval, and indexing to ground model outputs in enterprise knowledge. → Vector Databases (Pinecone, Weaviate, Qdrant, Chroma) Backbone of semantic search. They store embeddings and power retrieval in RAG, recommendations, and memory systems using fast nearest-neighbor algorithms. → Evaluation & Benchmarking (Fireworks AI Eval Protocol, Ragas, TruLens) Lets you test for accuracy, hallucinations, regressions, and preference alignment. Core to validating model behavior across prompts, versions, or fine-tuning runs. → Memory Systems (MEM-0, LangChain Memory, Milvus Hybrid) Enables agents to retain past interactions. Useful for building persistent assistants, session-aware tools, and long-term personalized workflows. → Agent Observability (LangSmith, HoneyHive, Arize AI Phoenix) Debugging LLM chains is non-trivial. These tools surface traces, logs, and step-by-step reasoning so you can inspect and iterate with confidence. → Fine-Tuning & Reward Stacks (PEFT, LoRA, Fireworks AI RLHF/RLVR) Supports adapting base models efficiently or aligning behavior using reward models. Great for domain tuning, personalization, and safety alignment. → Multimodal Toolkits (CLIP, BLIP-2, Florence-2, GPT-4o APIs) Text is just one modality. These toolkits let you build agents that understand images, audio, and video, enabling richer input/output capabilities. If you're deep in AI infra or systems, print this out, build a test project around each, and experiment with how they fit together. You’ll learn more in a weekend with these tools than from hours of reading docs. What’s one tool you’d add to this list? 👇 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI infrastructure insights, and subscribe to my newsletter for deeper technical breakdowns: 🔗 https://lnkd.in/dpBNr6Jg

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.

👋 Stefan Krawczyk and I put together a resource list to help folks ramp up for our Building LLM Apps for Data Scientists and Software Engineers course. But we realized it might be useful to a broader audience – so we’re sharing it! It’s a mix of Python, deep learning, evaluation, MLOps, and prompt engineering – all aimed at building reliable LLM-powered apps. If you're iterating beyond POCs, we hope this helps. The best part?  👉 Everything here is open and accessible. No paywalls, no subscriptions – just great content to dive into (even the books!). 📚 Resource List: 🐍 Python Data Science Handbook – Jake Vanderplas A deep dive into core Python libraries like pandas, NumPy, and scikit-learn – essential for modern data work. 📖 Fastbook – Jeremy Howard, Sylvain Gugger  A hands-on deep learning guide with PyTorch that explains not just "how," but "why." Perfect for expanding your LLM knowledge. 🧱 LLMs from Scratch – Sebastian Raschka, PhD  A practical guide to building LLMs from the ground up – perfect for those who want to understand model architectures and training workflows at a deeper level. ⚙️ MLOps vs DevOps – Ville Tuulos, Hugo Bowne-Anderson Why ML needs different workflows than traditional DevOps – essential for scaling LLM systems. 🧪 LLM Evaluation – Hamel Husain Practical techniques for evaluating LLM outputs and closing the gap between demos and production. 🔄 AI Engineering Flywheel – Shreya Shankar How to iteratively improve AI systems through logging, feedback, and evaluation. 💡 The Prompt Report – Sander Schulhoff and others  A comprehensive guide to prompting strategies and methods for better LLM performance. 🚀 Generative AI Platforms – Chip Huyen All about scaling GenAI apps – from infrastructure to deployment. 🔧 Applied-LLMs.org – Eugene Yan, Bryan Bischof, Charles Frye, Hamel Husain, Jason Liu, and Shreya Shankar Tools and case studies on real-world LLM deployments. This project distills lessons from practitioners actively deploying LLM-powered applications in production environments. 🛠️ Generative AI Guidebook – Ravin Kumar  A beginner-friendly roadmap for building generative AI systems step by step. 🤖 Building Effective Agents – Erik Schluntz, Barry Zhang, Anthropic A deep dive into building agentic LLM workflows that are autonomous and aligned. 🧩 What Are Embeddings? – Vicki Boykis  A comprehensive look at embeddings – their evolution and importance in ML. 👉 All links to these resources are available in this Google Doc: https://lnkd.in/gZa-iRT6 For the full course: Building LLM Apps for Data Scientists and Software Engineers https://lnkd.in/gBKePdph What would you add to the list?

In the last couple of months, I’ve “coded” a few internal apps to automate parts of my life—and I’ve leaned on LLMs (#Claude, #ChatGPT, #Gemini 2.5) to help sketch out architecture and write boilerplate. They do make mistakes and sometimes overcomplicate, but for an internal tool with only a handful of users, they get the job done. I can already see a real opportunity for no‑code builders and professionals to take these prototypes, tighten them up, and make them production‑ready. That’s why I found the recent Massachusetts Institute of Technology News story so exciting: researchers have devised a way to guide an LLM toward syntactically correct, error‑free code by embedding expert knowledge into the generation process—using a technique called sequential Monte Carlo. In practice, this means: ✅ The model spawns multiple “candidate” outputs in parallel. ✅ Each candidate is scored on structural validity (e.g., valid Python or SQL syntax) and semantic alignment (does it match the user’s intent?). ✅ We drop weak candidates early, doubling down on the ones most likely to work. With this probabilistic approach, even a small open‑source model can beat much larger commercial counterparts on real‑world tasks—from writing Python functions to crafting SQL queries, even designing molecular structures or robotics plans. In other words, MIT’s framework lets small LLMs punch well above their weight, making it easier for non‑specialists to get reliable code or data queries without learning arcane language rules first. For anyone experimenting with internal tools or exploring no‑code/low‑code solutions, this is a big deal. In the near future, I can imagine business users typing a natural‑language description and getting back a fully valid SQL query, or a developer quickly iterating on a microservice stub without worrying it’ll compile. Of course, human review remains essential—LLMs still trip over edge cases—but techniques like sequential Monte Carlo give us more confidence that AI can handle the “boring but necessary” bits of code, freeing us to focus on higher‑level architecture and innovation. If you’re curious about how they’re making small models more reliable, check out the full MIT News article here: https://lnkd.in/eiHhk4VX #AI #LLM #MachineLearning #NoCode #LowCode #Python #SQL #SequentialMonteCarlo #MITResearch #Innovation #AIDevelopment #TechLeadership

Achieving 3x-25x Performance Gains for High-Quality, AI-Powered Data Analysis Asking complex data questions in plain English and getting precise answers feels like magic, but it’s technically challenging. One of my jobs is analyzing the health of numerous programs. To make that easier we are building an AI app with Sapient Slingshot that answers natural language queries by generating and executing code on project/program health data. The challenge is that this process needs to be both fast and reliable. We started with gemini-2.5-pro, but 50+ second response times and inconsistent results made it unsuitable for interactive use. Our goal: reduce latency without sacrificing accuracy. The New Bottleneck: Tuning "Think Time" Traditional optimization targets code execution, but in AI apps, the real bottleneck is LLM "think time", i.e. the delay in generating correct code on the fly. Here are some techniques we used to cut think time while maintaining output quality: ① Context-Rich Prompts Accuracy starts with context. We dynamically create prompts for each query: ➜ Pre-Processing Logic: We pre-generate any code that doesn't need "intelligence" so that LLM doesn't have to ➜ Dynamic Data-Awareness: Prompts include full schema, sample data, and value stats to give the model a full view. ➜ Domain Templates: We tailor prompts for specific ontology like "Client satisfaction" or "Cycle Time" or "Quality". This reduces errors and latency, improving codegen quality from the first try. ② Structured Code Generation Even with great context, LLMs can output messy code. We guide query structure explicitly: ➜ Simple queries: Direct the LLM to generate a single line chained pandas expression. ➜ Complex queries : Direct the LLM to generate two lines, one for processing, one for the final result Clear patterns ensure clean, reliable output. ③ Two-Tiered Caching for Speed Once accuracy was reliable, we tackled speed with intelligent caching: ➜ Tier 1: Helper Cache – 3x Faster ⊙ Find a semantically similar past query ⊙ Use a faster model (e.g. gemini-2.5-flash) ⊙ Include the past query and code as a one-shot prompt This cut response times from 50+s to <15s while maintaining accuracy. ➜ Tier 2: Lightning Cache – 25x Faster ⊙ Detect duplicates for exact or near matches ⊙ Reuse validated code ⊙ Execute instantly, skipping the LLM This brought response times to ~2 seconds for repeated queries. ④ Advanced Memory Architecture ➜ Graph Memory (Neo4j via Graphiti): Stores query history, code, and relationships for fast, structured retrieval. ➜ High-Quality Embeddings: We use BAAI/bge-large-en-v1.5 to match queries by true meaning. ➜ Conversational Context: Full session history is stored, so prompts reflect recent interactions, enabling seamless follow-ups. By combining rich context, structured code, caching, and smart memory, we can build AI systems that deliver natural language querying with the speed and reliability that we, as users, expect of it.

Literally one of the best ways you can build LLM-based applications: Mirascope is an open-source library. Big selling point: This is not going to force abstractions down your throat. Instead, Mirascope gives you composable primitives for building with large language models. For example, • You can incorporate streaming into your application • Add support for tool calling • Handle structure outputs You can pick and choose what you need without worrying about unnecessary abstractions. Basically, this is a well-designed low-level API that you can use like Lego blocks. For example, attached, you can see a streaming agent with a tool calling in 11 lines of code. There are no magic classes here, or hidden state machines that do things you don't know about. This is just simple Python code. A few highlights: • It supports OpenAI, Anthropic, Google, and any other model. • You can swap providers by changing a string. • Agents are just tool calling in a while loop. • You always decide how your code operates. • Type-safe end-to-end. • Great autocomplete, catches errors before runtime. Mirascope is fully open source, MIT-licensed. Here is the GitHub repository: https://lnkd.in/eKeuqHww

Reasoning Models 2.0, combine Reasoning with Tool Use! ✨ START teaches LLMs to use tools, such as code interpreter to improve reasoning and problem-solving. Self-taught Reasoner with Tools (START) integrates tool usage with chain-of-thought reasoning by enabling tool calls, self-check, exploration, and self-debug while reasoning using a self-learning framework. 👀 Implementation 1️⃣ Collect math problems (AIME, MATH) and coding tasks (Codeforces, LiveCodeBench) 2️⃣ Create context-specific hints like "Maybe using Python here is a good idea" 3️⃣ Generate tool-assisted reasoning data (insert hints after conjunctions like "Wait" and before stop tokens) 6️⃣ Score trajectories, remove repetitive patterns, and create a seed dataset with successful tool-assisted reasoning examples. 7️⃣ Fine-tune model on seed dataset, then self self-Distill to generate more diverse reasoning trajectories 6️⃣ Fine-tune the base model using rejection sampling (RFT) on the extended dataset Insights 💡 Improves math accuracy by +15% (AMC23: 95.0%) and coding by +38.6% on medium problems. 📈 Test-time scaling via sequential hints boosts AIME24 performance by 12%. 🐞 Code template modification reduces debug errors by 41% in training data. 💡 Adding tools (Python interpreter) improves performance more than adding more training data. 🧠 Large models already possess latent tool-using abilities that can be activated through hints. 🛠️ Two-phase training (Hint-RFT then RFT) allows the model to learn effective tool usage. 📍 Hint place selection is important. After conjunction Token and before stop token. Paper: https://lnkd.in/emF_m8Qz

Ask your LLM the following question: "How many zeros are in 0101010101010101101?". A typical LLM might hallucinate the answer because it’s just predicting tokens. Now let’s raise the stakes: "What’s the current stock price of Google, and what was its 5-day average at market close?" To answer this, most LLMs must: 1. Pause to call a financial data API 2. Pause again to calculate the average 3. Possibly pause once more to format the result That’s multiple tool calls, each interrupting the thought process, adding latency, re-sending the entire conversation history and increasing cost. Enter CodeAgents. Instead of hallucinating an answer or pausing after every step, CodeAgents allow the LLM to translate its entire plan into executable code. It reasons through the problem, writes the script, and only then executes. Clean, efficient, and accurate. This results in: 1. Fewer hallucinations 2. Smarter, end-to-end planning 3. Lower latency 4. More reliable answers If you're exploring how to make LLMs think in code and solve multi-step tasks efficiently, check out the following: Libraries: - https://lnkd.in/g6wa_Wm4 - https://lnkd.in/gcuf2u5Q Course: - https://lnkd.in/gTse8tTw #AI #LLM #CodeAgents

Like many, I've been spending some time experimenting with and learning about LLMs (large language models), particularly about how developers not familiar with AI can integrate AI into their existing web apps. One of the most popular use cases for LLMs is performing semantic search over custom documents that ChatGPT isn't trained on (or documents that are too long to paste into ChatGPT). Imagine having a private GPT instance that has access to your org's internal knowledge base that you can ask questions about certain information. I wrote up a tutorial and sample application in which you can learn to do exactly this: 1. User can upload a private/custom document 2. Perform ChatGPT Q&A style interaction with the doc 3. Highlight the contents of the answer in the original doc This sample app is full-stack web app built using OpenAI GPT-3.5, Pinecone vector databse, LangChain, and Vercel NextJS. Great for app developers who are looking to learn more about adding AI to their apps. Gitub repo: https://lnkd.in/eQvpvE3K Medium article: https://lnkd.in/e25kfWnw

I work at Airbnb where I write 99% of my code with LLMs. One thing you need to understand is they only write shit code if you let them. When you're building high quality production software, writing code is always the 𝗹𝗮𝘀𝘁 𝘀𝘁𝗲𝗽. Your first step is to understand the problem that needs to be solved. Then ideate solutions, consider alternatives, explore tradeoffs and refine your exploration into a concrete plan. Even as you implement the plan task by task you should not be coding a stream of conscious. That leads to bad code design. You should be considering the architecture of the code, abstractions and coming up with a clean way to write it. Only after all this upfront design and planning work do you then start manually typing code with your fingers. That last step is not necessary to do manually anymore. Whenever I think of coding, I immediately reach for an LLM because I use it like a power tool. A carpenter does not leave their power drill on the table when they need to screw in a bolt. Why would you not use an LLM to execute on your plan? You are in the driver's seat, providing direct technical guidance at every step. 𝗬𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝘀𝗸𝗶𝗹𝗹 𝗹𝗲𝘃𝗲𝗹 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗺𝗽𝗮𝗰𝘁 𝗵𝗼𝘄 𝗴𝗼𝗼𝗱 𝘁𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗶𝘀. No, this is not slower than doing it without LLMs. You should also use LLMs as power tools for research, planning and architecture. This will get you even higher quality software than without them. It allows you to go far beyond due diligence and truly explore, analyze and refine your design fully before any single line of code is written. I use the following workflow to naturally research, design and plan the feature I want to build in the form of a conversation which then gets converted to a formal Spec that the LLM can implement task by task:  1. Explain the problem to the LLM.  2. Give it your ideas for the initial solution  3. Tell it explicitly: “Propose an approach first. Show alternatives to my solution, highlight tradeoffs. Do not write code until I approve.”  4. Review the proposal, poke holes in it, iterate  5. Tell it to write the plan to disk as a spec so you can hand off to another session later  6. Lastly, let it generate code. This is an excerpt from my article “Writing High Quality Production Code With LLMs Is A Solved Problem” full article here on LinkedIn —> https://lnkd.in/d3v-i9iK