LLM Strategies for Human-Level Performance

Most people still think of LLMs as “just a model.” But if you’ve ever shipped one in production, you know it’s not that simple. Behind every performant LLM system, there’s a stack of decisions, about pretraining, fine-tuning, inference, evaluation, and application-specific tradeoffs. This diagram captures it well: LLMs aren’t one-dimensional. They’re systems. And each dimension introduces new failure points or optimization levers. Let’s break it down: 🧠 Pre-Training Start with modality. → Text-only models like LLaMA, UL2, PaLM have predictable inductive biases. → Multimodal ones like GPT-4, Gemini, and LaVIN introduce more complex token fusion, grounding challenges, and cross-modal alignment issues. Understanding the data diet matters just as much as parameter count. 🛠 Fine-Tuning This is where most teams underestimate complexity: → PEFT strategies like LoRA and Prefix Tuning help with parameter efficiency, but can behave differently under distribution shift. → Alignment techniques- RLHF, DPO, RAFT, aren’t interchangeable. They encode different human preference priors. → Quantization and pruning decisions will directly impact latency, memory usage, and downstream behavior. ⚡️ Efficiency Inference optimization is still underexplored. Techniques like dynamic prompt caching, paged attention, speculative decoding, and batch streaming make the difference between real-time and unusable. The infra layer is where GenAI products often break. 📏 Evaluation One benchmark doesn’t cut it. You need a full matrix: → NLG (summarization, completion), NLU (classification, reasoning), → alignment tests (honesty, helpfulness, safety), → dataset quality, and → cost breakdowns across training + inference + memory. Evaluation isn’t just a model task, it’s a systems-level concern. 🧾 Inference & Prompting Multi-turn prompts, CoT, ToT, ICL, all behave differently under different sampling strategies and context lengths. Prompting isn’t trivial anymore. It’s an orchestration layer in itself. Whether you’re building for legal, education, robotics, or finance, the “general-purpose” tag doesn’t hold. Every domain has its own retrieval, grounding, and reasoning constraints. ------- Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

LLMs are no longer just fancy autocomplete engines. We’re seeing a clear shift—from single-shot prompting to techniques that mimic 𝗮𝗴𝗲𝗻𝗰𝘆: reasoning, retrieving, taking action, and even coordinating across steps. In this visual, I’ve laid out five core prompting strategies: - 𝗥𝗔𝗚 – Brings in external knowledge, enhancing factual accuracy   - 𝗥𝗲𝗔𝗰𝘁 – Enables reasoning 𝗮𝗻𝗱 acting, the essence of agentic behavior   - 𝗗𝗦𝗣 – Adds directional hints through policy models   - 𝗧𝗼𝗧 (𝗧𝗿𝗲𝗲-𝗼𝗳-𝗧𝗵𝗼𝘂𝗴𝗵𝘁) – Simulates branching reasoning paths, like a mini debate inside the LLM   - 𝗖𝗼𝗧 (𝗖𝗵𝗮𝗶𝗻-𝗼𝗳-𝗧𝗵𝗼𝘂𝗴𝗵𝘁) – Breaks down complex thinking into step-by-step logic While not all of these are fully agentic on their own, techniques like 𝗥𝗲𝗔𝗰𝘁 and 𝗧𝗼𝗧 are clear stepping stones to 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 — where autonomous agents can 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗽𝗹𝗮𝗻, 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁 𝘄𝗶𝘁𝗵 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀. The big picture?  We’re slowly moving from "𝘱𝘳𝘰𝘮𝘱𝘵 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨" to "𝘤𝘰𝘨𝘯𝘪𝘵𝘪𝘷𝘦 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘥𝘦𝘴𝘪𝘨𝘯." And that’s where the real innovation lies.

LLM pro tip to reduce hallucinations and improve performance: instruct the language model to ask clarifying questions in your prompt. Add a directive like "If any part of the question/task is unclear or lacks sufficient context, ask clarifying questions before providing an answer" to your system prompt. This will: (1) Reduce ambiguity - forcing the model to acknowledge knowledge gaps rather than filling them with hallucinations (2) Improve accuracy - enabling the model to gather necessary details before committing to an answer (3) Enhance interaction - creating a more natural, iterative conversation flow similar to human exchanges This approach was validated in the 2023 CALM paper, which showed that selectively asking clarifying questions for ambiguous inputs increased question-answering accuracy without negatively affecting responses to unambiguous queries https://lnkd.in/gnAhZ5zM

🧠 We just implemented the "third paradigm" for LLM learning - and the results are promising. Most of us know that leading AI applications like ChatGPT, Claude, and Grok achieve their impressive performance partly through sophisticated system prompts containing detailed reasoning strategies and problem-solving frameworks. Yet most developers and researchers work with basic prompts, missing out on these performance gains. 🚀 Introducing System Prompt Learning (SPL) Building on Andrej Karpathy's vision of a "third paradigm" for LLM learning, SPL enables models to automatically learn and improve problem-solving strategies through experience, rather than relying solely on pre-training or fine-tuning. ⚙️ How it works: 🔍 Automatically classifies incoming problems into 16 types 📚 Builds a persistent database of effective solving strategies 🎯 Selects the most relevant strategies for each new query 📊 Evaluates strategy effectiveness and refines them over time 👁️ Maintains human-readable, inspectable knowledge 📈 Results across mathematical benchmarks: OptILLMBench: 61% → 65% (+4%) MATH-500: 85% → 85.6% (+0.6%) Arena Hard: 29% → 37.6% (+8.6%) AIME24: 23.33% → 30% (+6.67%) After just 500 training queries, our system developed 129 strategies, refined 97 existing ones, and achieved 346 successful problem resolutions. ✨ What makes this approach unique: 🔄 Cumulative learning that improves over time 📖 Transparent, human-readable strategies 🔌 Works with any OpenAI-compatible API 🔗 Can be combined with other optimization techniques ⚡ Operates in both inference and learning modes 📝 Example learned strategy for word problems: 1. Understand: Read carefully, identify unknowns 2. Plan: Define variables, write equations 3. Solve: Step-by-step with units 4. Verify: Check reasonableness This represents early progress toward AI systems that genuinely learn from experience in a transparent, interpretable way - moving beyond static models to adaptive systems that develop expertise through practice. 🛠️ Implementation: SPL is available as an open-source plugin in optillm, our inference optimization proxy. Simple integration by adding "spl-" prefix to your model name. The implications extend beyond current capabilities - imagine domain-specific expertise development, collaborative strategy sharing, and human expert contributions to AI reasoning frameworks. 💭 What are your thoughts on LLMs learning from their own experience? Have you experimented with advanced system prompting in your work? #ArtificialIntelligence #MachineLearning #LLM #OpenSource #TechInnovation #ProblemSolving #AI #Research

Are humans 5X better than AI? This paper is blowing up (not in a good way) The recent study claims LLMs are 5x less accurate than humans at summarizing scientific research. That’s a bold claim. But maybe it’s not the model that’s off. Maybe it's the AI strategy, system, prompt, data... What’s your secret sauce for getting the most out of an llm? Scientific summarization is dense, domain-specific, and context-heavy. And evaluating accuracy in this space? That’s not simple either. So just because a general-purpose LLM is struggling with a turing style test... doesn't mean it can't do better. Is it just how they're using it? I think it's short sighted to drop a complex task into an LLM and expect expert results without expert setup. To get better answers, you need a better AI strategy, system, and deployment. Some tips and tricks we find helpful: 1. Start small and be intentional. Don’t just upload a paper and say “summarize this.” Define the structure, tone, and scope you want. Try prompts like: “List three key findings in plain language, and include one real-world implication for each.” The clearer your expectations, the better the output. 2. Test - Build in a feedback loop from the beginning. Ask the model what might be missing from the summary, or how confident it is in the output. Compare responses to expert-written summaries or benchmark examples. If the model can’t handle tasks where the answers are known, it’s not ready for tasks where they’re not. 3. Tweak - Refine everything: prompts, data, logic. Add retrieval grounding so the model pulls from trusted sources instead of guessing. Fine-tune with domain-specific examples to improve accuracy and reduce noise. Experiment with prompt variations and analyze how the answers change. Tuning isn’t just technical. Its iterative alignment between output and expectation. (Spoiler alert: you might be at this stage for a while.) 4. Repeat Every new domain, dataset, or objective requires a fresh approach. LLMs don’t self-correct across contexts, but your workflow can. Build reusable templates. Create consistent evaluation criteria. Track what works, version your changes, and keep refining. Improving LLM performance isn’t one and done. It’s a cycle. Finally: If you treat a language model like a magic button, it's going to kill the rabbit in the hat. If you treat it like a system you deploy, test, tweak, and evolve It can retrieve magic bunnies flying everywhere Q: How are you using LLMs to improve workflows? Have you tried domain-specific data? Would love to hear your approaches in the comments. 

🔔 #ALERT Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey ➡️ Complex problem solving is framed from both cognitive science (human-centered trace) and computational theory (algorithm design) perspectives. ➡️ Key challenges for LLMs in this space are multi-step reasoning, effective domain knowledge integration, and reliable result verification. ➡️ Methodologies discussed include enhancing Chain-of-Thought reasoning via data synthesis and self-correction, leveraging external knowledge bases (RAG, KGs), and employing diverse verification tools (LLM-as-a-judge, symbolic, experimental). ➡️ The survey maps these challenges and advancements to specific domains: software engineering, mathematics, data science, and scientific research, highlighting domain-specific complexities. ➡️ Future directions emphasize addressing data scarcity, reducing computational costs, improving knowledge representation, and developing more robust evaluation frameworks for complex, open-ended problems. Large Language Models demonstrate capabilities for complex problem solving by approximating human-like reasoning and integrating computational tools. However, deploying them effectively in real-world scenarios requires overcoming significant hurdles. The survey highlights that while progress has been made in areas like multi-step reasoning through techniques like Chain-of-Thought and self-correction, challenges remain in handling complex sequences and ensuring high accuracy. Integrating specialized domain knowledge is critical, moving beyond pre-training to using external sources and agent-based approaches. Furthermore, reliable verification of solutions, especially in domains lacking clear outcomes, necessitates a combination of LLM-based, symbolic, and experimental methods. The path forward involves refining these core capabilities and tailoring solutions to the unique demands of different technical fields. If you are keeping track of where the industry and the implementation of the AI is at! This article from ANTgroup and Zhejiang University is for you. #LLMs #TechnicalSurvey #ProblemSolving #ArtificialIntelligence