Optimization Techniques for Artificial Intelligence

If you’re an AI engineer trying to optimize your LLMs for inference, here’s a quick guide for you 👇 Efficient inference isn’t just about faster hardware, it’s a multi-layered design problem. From how you compress prompts to how your memory is managed across GPUs, everything impacts latency, throughput, and cost. Here’s a structured taxonomy of inference-time optimizations for LLMs: 1. Data-Level Optimization Reduce redundant tokens and unnecessary output computation. → Input Compression:  - Prompt Pruning, remove irrelevant history or system tokens  - Prompt Summarization, use model-generated summaries as input  - Soft Prompt Compression, encode static context using embeddings  - RAG, replace long prompts with retrieved documents plus compact queries → Output Organization:  - Pre-structure output to reduce decoding time and minimize sampling steps 2. Model-Level Optimization (a) Efficient Structure Design → Efficient FFN Design, use gated or sparsely-activated FFNs (e.g., SwiGLU) → Efficient Attention, FlashAttention, linear attention, or sliding window for long context → Transformer Alternates, e.g., Mamba, Reformer for memory-efficient decoding → Multi/Group-Query Attention, share keys/values across heads to reduce KV cache size → Low-Complexity Attention, replace full softmax with approximations (e.g., Linformer) (b) Model Compression → Quantization:  - Post-Training, no retraining needed  - Quantization-Aware Training, better accuracy, especially <8-bit → Sparsification:  - Weight Pruning, Sparse Attention → Structure Optimization:  - Neural Architecture Search, Structure Factorization → Knowledge Distillation:  - White-box, student learns internal states  - Black-box, student mimics output logits → Dynamic Inference, adaptive early exits or skipping blocks based on input complexity 3. System-Level Optimization (a) Inference Engine → Graph & Operator Optimization, use ONNX, TensorRT, BetterTransformer for op fusion → Speculative Decoding, use a smaller model to draft tokens, validate with full model → Memory Management, KV cache reuse, paging strategies (e.g., PagedAttention in vLLM) (b) Serving System → Batching, group requests with similar lengths for throughput gains → Scheduling, token-level preemption (e.g., TGI, vLLM schedulers) → Distributed Systems, use tensor, pipeline, or model parallelism to scale across GPUs My Two Cents 🫰 → Always benchmark end-to-end latency, not just token decode speed → For production, 8-bit or 4-bit quantized models with MQA and PagedAttention give the best price/performance → If using long context (>64k), consider sliding attention plus RAG, not full dense memory → Use speculative decoding and batching for chat applications with high concurrency → LLM inference is a systems problem. Optimizing it requires thinking holistically, from tokens to tensors to threads. Image inspo: A Survey on Efficient Inference for Large Language Models ---- Follow me (Aishwarya Srinivasan) for more AI insights!

After optimizing costs for many AI systems, I've developed a systematic approach that consistently delivers cost reductions of 60-80%. Here's my playbook, in order of least to most effort: Step 1: Optimizing Inference Throughput Start here for the biggest wins with least effort. Enabling caching (LiteLLM (YC W23), Zilliz) and strategic batch processing can reduce costs by a lot with very little effort. I have seen teams cut costs by half simply by implementing caching and batching requests that don't require real-time results. Step 2: Maximizing Token Efficiency This can give you an additional 50% cost savings. Prompt engineering, automated compression (ScaleDown), and structured outputs can cut token usage without sacrificing quality. Small changes in how you craft prompts can lead to massive savings at scale. Step 3: Model Orchestration Use routers and cascades to send prompts to the cheapest and most effective model for that prompt (OpenRouter, Martian). Why use GPT-4 for simple classification when GPT-3.5 will do? Smart routing ensures you're not overpaying for intelligence you don't need. Step 4: Self-Hosting I only suggest self-hosting for teams at scale because of the complexities involved. This requires more technical investment upfront but pays dividends for high-volume applications. The key is tackling these layers systematically. Most teams jump straight to self-hosting or model switching, but the real savings come from optimizing throughput and token efficiency first. What's your experience with AI cost optimization?

Thought you know all about DP? Here’s an expanded tour of DP optimization techniques, from the fundamentals all the way to advanced tricks: 1. Top-Down vs. Bottom-Up 🔹 Memoization (recursion + cache) 🔹 Tabulation (iterative table filling) 2. Space-Saving Strategies 🔹 Rolling arrays: Keep only the last one or two rows (or dimensions) of your DP table. 🔹 Bitsets: Pack small states into bit operations for ultra-fast transitions. 3. Prefix-Sum & Difference Tricks 🔹 Precompute cumulative sums to reduce O(N) transition loops to O(1). 🔹 Use difference arrays for range-update patterns in DP. 4. Monotonic Queue / Sliding Window 🔹 For “min/max over last K states” problems, maintain a deque of candidates in amortized O(1) per update. 5. Bitmask & SOS-DP 🔹 Bitmask DP for subsets of up to ~20 elements (2ⁿ states). 🔹 SOS (Sum Over Subsets) DP to compute functions on all subsets via fast zeta transforms. 6. Segment-Tree-Backed DP 🔹 Use a segment tree (or Fenwick tree) to answer range min/max queries or do range updates on your DP array in O(log N). 🔹 Merge DP states efficiently when you need non-trivial transitions over intervals. 7. 1D/1D (Monge or Quadrangle-Inequality) Optimization 🔹 Targets recurrences of the form dp[i] = min_{0 ≤ j < i} [dp[j] + w(j, i)] where w satisfies the quadrangle (Monge) inequality, so the argmin indices k(i) are non-decreasing. 🔹 Use divide-and-conquer to compute all dp[i] in O(N log N), or Knuth’s optimization to push it to O(N) when stronger conditions hold . 8. Divide-and-Conquer Optimization 🔹 A special case of 1D/1D when optimal split points are monotonic: drop O(N²) down to O(N log N) by recursively solving on segments and narrowing search ranges. 9. Knuth / Quadrangle Inequality 🔹 When cost functions satisfy the quadrangle inequality and boundary conditions, you can reduce range-DP from O(N³) to O(N²) (or even to O(N) in certain forms). 10. Convex Hull Trick & Li Chao Tree 🔹 Optimize linear recurrences of the form dp[i] = min_j [m_j * x_i + b_j] from O(N²) to O(N log N) (or O(N) with a monotonic hull). 11. FFT-Based Convolution 🔹 Use fast polynomial multiplication (FFT) to merge DP steps in O(N log N) instead of O(N²). 12. Matrix Exponentiation / Chain Exponentiation 🔹 Model linear recurrences as dp_vec[i] = M * dp_vec[i−1] Raise the transition matrix M to the nᵗʰ power in O(k³ log n) (or faster) to compute dp[n] in logarithmic time. 13. Berlekamp–Massey Algorithm 🔹 Given the first 2k terms of a sequence, extract its minimal linear recurrence in O(k²). 🔹 Combine with fast exponentiation to compute the nᵗʰ term in O(k² log n), even for very large n. 14. Slope Trick & Aliens’ Tricks 🔹 Handle piecewise-linear DP functions and complex cost updates by maintaining envelopes of slopes. 🔹 Ideal for “add a V-shaped penalty” or “minimize sum of absolute deviations plus a quadratic cost.” Mastering these tools will raise your problem-solving skills, whether you’re in a contest or a interview.

Overview of Self-Evolving Agents There is a growing interest in moving from hand-crafted agentic systems to lifelong, adaptive agentic ecosystems. What's the progress, and where are things headed? Let's find out: ● Overview This survey defines self-evolving AI agents and argues for a shift from static, hand-crafted systems to lifelong, adaptive agentic ecosystems. It maps the field’s trajectory, proposes “Three Laws” to keep evolution safe and useful, and organizes techniques across single-agent, multi-agent, and domain-specific settings. ● Paradigm shift and guardrails The paper frames four stages: Model Offline Pretraining → Model Online Adaptation → Multi-Agent Orchestration → Multi-Agent Self-Evolving. It introduces three guiding laws for evolution: maintain safety, preserve or improve performance, and then autonomously optimize. ● The Evolution Landscape of AI Agents The paper presents a visual taxonomy of AI agent evolution and optimisation techniques, categorised into three major directions: single-agent optimisation, multi-agent optimisation, and domain-specific optimisation. ● Unified framework for evolution A single iterative loop connects System Inputs, Agent System, Environment feedback, and Optimizer. Optimizers search over prompts, tools, memory, model parameters, and even agent topologies using heuristics, search, or learning. ● Single-agent optimization toolbox Techniques are grouped into: (i) LLM behavior (training for reasoning; test-time scaling with search and verification), (ii) prompt optimization (edit, generate, text-gradient, evolutionary), (iii) memory optimization (short-term compression and retrieval; long-term RAG, graphs, and control policies), and (iv) tool use and tool creation. ● Agentic Self-Evolution methods The authors present a comprehensive hierarchical categorization of agentic self-evolution methods, including single-agent, multi-agent, and domain-specific optimization categories. ● Multi-agent workflows that self-improve Beyond manual pipelines, the survey treats prompts, topologies, and backbones as searchable spaces. It distinguishes code-level workflows and communication-graph topologies, covers unified optimization that jointly tunes prompts and structure, and describes backbone training for better cooperation. ● Evaluation, safety, and open problems Benchmarks span tools, web navigation, GUI agents, collaboration, and specialized domains; LLM-as-judge and Agent-as-judge reduce evaluation cost while tracking process quality.

🚀 Fine-Tuning vs RAG vs Agentic AI vs Context Engineering These techniques solve different layers of the AI stack. Choosing the right one depends on what you want to optimize: learning, knowledge access, action, or control. 1. Fine-Tuning Updates the model’s weights using domain-specific data. Example: Training a base LLM on legal contracts so it understands legal language and structure. When to use - Domain is stable and well-defined - You need consistent behavior at scale - The model must internalize new patterns Tradeoffs - High training cost - Slow to update when knowledge changes - Risk of overfitting or catastrophic forgetting Performance metrics - Task accuracy or F1 - Loss convergence - Hallucination rate - Human evaluation score 2. Retrieval-Augmented Generation (RAG) Retrieves relevant documents at query time and injects them into the prompt. Example: An internal knowledge assistant that searches company policies before answering. When to use - Knowledge changes frequently - Data is large or distributed - Answers must be source-grounded Tradeoffs - Retrieval latency - Dependency on embedding and search quality - Context window limitations Performance metrics - Retrieval precision and recall - Answer correctness - Latency per query - Source attribution rate 3. Agentic AI Allows the model to plan, call tools, and execute multi-step workflows. Example: An AI that pulls financial data, runs analysis, generates charts, and emails a report. When to use - Tasks require multiple decisions - External tools or APIs are needed - Automation is the goal, not just text Tradeoffs - Harder to debug - Higher operational complexity - Risk of cascading failures Performance metrics - Task success rate - Step completion rate - Execution latency - Error and retry rate 4. Context Engineering Shapes output through prompts, examples, rules, and formatting. Example: Providing structured prompts so the model always outputs JSON in a fixed schema. When to use - You need fast iteration - No retraining budget or time - Output format and control matter Tradeoffs - Fragile to prompt changes - Hard to scale across many tasks - Behavior may drift across models Performance metrics - Format compliance rate - Prompt success rate - Manual correction rate - Response consistency 💡 How to think about this - Fine-Tuning changes what the model knows - RAG changes what the model can look up - Agentic AI changes what the model can do - Context Engineering changes how the model responds Strong AI systems usually combine all four. ➕ Follow Shyam Sundar D. for practical learning on Data Science, AI, ML, and Agentic AI 📩 Save this post for future reference ♻ Repost to help others learn and grow in AI #ArtificialIntelligence #GenerativeAI #LLM #RAG #AgenticAI #FineTuning #ContextEngineering #AIEngineering #MLOps #DataScience

Nothing changed in the product. But the AI bill doubled overnight. That’s when most teams learn the hard truth: 𝐭𝐨𝐤𝐞𝐧 𝐮𝐬𝐚𝐠𝐞 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐞𝐱𝐩𝐥𝐨𝐝𝐞 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐨𝐧𝐞 𝐛𝐢𝐠 𝐦𝐢𝐬𝐭𝐚𝐤𝐞, 𝐢𝐭 𝐜𝐫𝐞𝐞𝐩𝐬 𝐢𝐧 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐝𝐨𝐳𝐞𝐧𝐬 𝐨𝐟 𝐬𝐦𝐚𝐥𝐥 𝐨𝐧𝐞𝐬. Here’s a simple breakdown of the core strategies that keep AI systems fast, affordable, and predictable as they scale: 𝐂𝐨𝐬𝐭 𝐑𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐅𝐨𝐜𝐮𝐬 ‣ Shorten System Prompts Cut the unnecessary instructions. Smaller system prompts mean lower cost on every single call. ‣ Use Structured Prompts Bullets, schemas, and clear formats reduce ambiguity and prevent the model from generating long, wasteful responses. ‣ Trim Conversation History Only include the parts relevant to the current task. Long-running agents often burn tokens without you noticing. ‣ Budget Your Context Window Divide context into strict sections so one part doesn’t overwhelm the whole window. 𝐋𝐚𝐭𝐞𝐧𝐜𝐲 & 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐅𝐨𝐜𝐮𝐬 ‣ Compress Retrieved Content Summaries → key chunks → only then full text. This keeps retrieval grounded without ballooning token usage. ‣ Metadata-First Retrieval Start with summaries or metadata; pull full documents only when required. ‣ Replace Text with IDs Instead of resending repeated text, reference IDs, states, or steps. ‣ Limit Tool Output Size Filter tool returns so agents only receive the data they actually need. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 & 𝐒𝐩𝐞𝐞𝐝 𝐅𝐨𝐜𝐮𝐬 ‣ Use Smaller Models Smartly Not every step needs your biggest model. Route simple tasks to lighter ones. ‣ Stop Over-Explaining If you don’t ask for long reasoning, the model won’t generate it. Huge hidden token savings. ‣ Cache Stable Responses If an instruction doesn’t change, don’t regenerate it. Cache it. ‣ Enforce Max Output Tokens Set strict caps so the model never produces more than required. Costs rarely spike because AI got more expensive, they spike because your system became less disciplined. Optimizing tokens isn’t optional anymore. It’s how you build AI products that scale without burning your budget.

Stuck on your current AI model because switching feels like too much work? We just shipped automated prompt optimization in Freeplay to solve exactly this problem. We see the following patterns constantly: Teams do weeks of prompt engineering to make GPT-4o work well for their use case. Then Gemini 2.5 Flash comes out with the promise of better performance or cost, but nobody wants to re-optimize all their prompts from scratch. So they stay stuck on the old model, even when better options exist. Or: A PM see the same set of recurring problems with production prompts and wants to try out some changes, but doesn't feel confident about all the latest prompt engineering best practices. It can feel like a never-ending set of tweaks trying to make things incrementally better, but is it worth it? And could it happen faster? ✨ A better approach: Use your production data to automate prompt engineering. We've been experimenting with more and more uses of AI in Freeplay, and this one consistently works: 1. Decide which prompt you want to optimize and which model you want to optimize for. Write some short instructions if you'd like about what you want to change. 2. Use production data including logs with auto-eval scores, customer feedback, and human labels from your team as inputs to automatically generate optimized prompts with Freeplay's agent. 3. Instantly launch a test with your preferred dataset and your custom eval criteria to see how the new, optimized prompt & model combo compares to your old one. Compare any prompt version and model head-to-head (Claude Sonnet 4 vs Opus 4.1, GPT vs Gemini, etc.). 4. Get detailed explanations of every change and view side-by-side diffs for further validation. All the changes are fully transparent, and you can keep iterating by hand as you'd like. Instead of manual hours analyzing logs and running experiments, your production evaluation results, customer feedback, and human annotations become fuel for continuous optimization. How it works: Click "Optimize" on any prompt → Our agent analyzes your production data → Get an optimized version with diff view → Auto-run your evals to validate improvements More like this coming soon! The future of AI product development will be increasingly automated optimization workflows, where agents help evaluate and improve other agents. Try it now if you're a Freeplay customer - just click "Optimize" on any prompt. #AIProductDevelopment #PromptEngineering #ProductStrategy #AutomatedOptimization #LLMs

To move from a weekend AI demo to a AI production-grade application, you need to architect these 4 layers. Most people stop at the prompt. That is a mistake. Here is the technical blueprint for a production-grade system: 𝟭. 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗖𝗼𝗿𝗲 (𝗧𝗵𝗲 𝗕𝗿𝗮𝗶𝗻) Your LLM needs a loop, not just a prompt. ⏹︎ Execution Loops: Implement a "Thought > Action > Observation" cycle. ⏹︎ State Management: Don't rely on model memory. Use Redis or Postgres for persistent context. ⏹︎ Tool Registry: Connect the core to APIs and Python environments using frameworks like LangChain or LlamaIndex. 𝟮. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗥𝗔𝗚 (𝗧𝗵𝗲 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲) Naive RAG fails in production. You need a multi-step pipeline. ⏹︎ Ingestion: Move from fixed chunking to semantic or hierarchical chunking. ⏹︎ Retrieval: Vector search is insufficient. Implement Hybrid Search (Keyword + Semantic) for accuracy. ⏹︎ Refinement: Always apply Reranking Models to filter results from databases like Pinecone or Qdrant. 𝟯. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 (𝗧𝗵𝗲 𝗦𝗰𝗮𝗹𝗲) Latency kills user experience. You need high-performance serving. ⏹︎ Orchestration: Containerize with Docker and manage scale via Kubernetes. ⏹︎ Serving Layer: Use Ray Serve and FastAPI to handle concurrent requests. ⏹︎ Model Hosting: Optimize inference using vLLM or TGI. 𝟰. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗛𝗲𝗮𝗹𝘁𝗵) If you cannot measure it, you cannot trust it. ⏹︎ Tracing: Use LangSmith or Arize to debug complex agent chains. ⏹︎ Evaluation: mathematically score your outputs using Ragas or TruLens. ⏹︎ Optimization: Reduce latency with Quantization (GGML/GGUF) or domain-adapt using PEFT techniques like LoRA. 𖤂 Repost to help your network move beyond simple wrappers. I’m Nina. I build with AI and share how it’s done weekly. #agentic #llm #softwaredevelopment #technology

🚀 New Paper Alert! Excited to share our latest paper, "Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications", now available on arXiv - https://lnkd.in/dZrUEGqD! Large Language Models (LLMs) have unlocked incredible capabilities across AI applications, from search and recommendations to generative tasks. However, their sheer size and computational cost often make them impractical for real-world deployment at scale. In this work, we explore techniques to train and deploy Small Language Models (SLMs) that retain much of the power of their larger counterparts while being significantly more efficient. 🔍 Key Contributions: ✅ Knowledge Distillation – We efficiently transfer knowledge from large models to smaller ones, ensuring strong task performance. We demonstrate the effectiveness of various flavors of distillation - on-policy, supervised, seqKD ✅ Model Compression (Pruning & Quantization) – We apply structured pruning (OSSCAR) and quantization (GPTQ, QuantEase, FP8) to drastically reduce model size while maintaining accuracy. ✅ Real-World Deployment at LinkedIn – We showcase how we deploy SLMs for ranking, recommendation, and reasoning tasks at LinkedIn, achieving 20× model compression with minimal accuracy loss. ✅ Serving Optimizations – We detail inference speedups, leveraging techniques like RadixAttention, FlashInfer, and tensor parallelism on NVIDIA H100 GPUs to optimize latency and throughput. Key Results: 📉 20× reduction in model size with minimal accuracy loss ⚡ 40% improvement in attention latency through structured pruning 🚀 Significant serving speedup with FP8 quantization and prefix caching This work is a step toward making LLMs more efficient, scalable, and production-ready for industry use cases. We hope it helps others looking to deploy high-performance AI at scale! A huge shoutout to my incredible co-authors at LinkedIn for their contributions - Qingquan Song, Kayhan Behdin, Yun Dai, Ata Fatahi, Shao Tang, HEJIAN SANG, Gregory Dexter, Sirou Z., Jason (Siyu) Zhu, Tejas Dharamsi, Maziar Sanjabi, Vignesh Kothapalli, Hamed Firooz, Zhoutong Fu, Yihan Cao, Pin-Lun (Byron) Hsu, Fedor Borisyuk, Zhipeng(Jason) Wang, PhD, Rahul Mazumder, Natesh Pillai, Luke Simon Special thanks to our leadership Zhipeng(Jason) Wang, PhD, Xiaobing Xue, Necip Fazil Ayan, Deepak Agarwal for empowering us and helping us push the envelope! #AI #MachineLearning #LLMs #Efficiency #AIatScale #DeepLearning #KnowledgeDistillation #Pruning #Quantization #Deployment