Increasing LLM Pattern Matching Accuracy

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.

In the last three months alone, over ten papers outlining novel prompting techniques were published, boosting LLMs’ performance by a substantial margin. Two weeks ago, a groundbreaking paper from Microsoft demonstrated how a well-prompted GPT-4 outperforms Google’s Med-PaLM 2, a specialized medical model, solely through sophisticated prompting techniques. Yet, while our X and LinkedIn feeds buzz with ‘secret prompting tips’, a definitive, research-backed guide aggregating these advanced prompting strategies is hard to come by. This gap prevents LLM developers and everyday users from harnessing these novel frameworks to enhance performance and achieve more accurate results. https://lnkd.in/g7_6eP6y In this AI Tidbits Deep Dive, I outline six of the best and recent prompting methods: (1) EmotionPrompt - inspired by human psychology, this method utilizes emotional stimuli in prompts to gain performance enhancements (2) Optimization by PROmpting (OPRO) - a DeepMind innovation that refines prompts automatically, surpassing human-crafted ones. This paper discovered the “Take a deep breath” instruction that improved LLMs’ performance by 9%. (3) Chain-of-Verification (CoVe) - Meta's novel four-step prompting process that drastically reduces hallucinations and improves factual accuracy (4) System 2 Attention (S2A) - also from Meta, a prompting method that filters out irrelevant details prior to querying the LLM (5) Step-Back Prompting - encouraging LLMs to abstract queries for enhanced reasoning (6) Rephrase and Respond (RaR) - UCLA's method that lets LLMs rephrase queries for better comprehension and response accuracy Understanding the spectrum of available prompting strategies and how to apply them in your app can mean the difference between a production-ready app and a nascent project with untapped potential. Full blog post https://lnkd.in/g7_6eP6y

Can we finetune our LLM and retriever together to improve RAG performance? This paper proposes a technique to do exactly that! RAG Basics: When you prompt an LLM, RAG supplies relevant documents. A separate retrieval model computes the probability of each text chunk being relevant and provides the top chunks to the LLM. The LLM generates tokens based on the chunks, prompt, and previous tokens. In Short: Fine-tuning LLMs and retrieval models together improves performance without extensive data processing, enabling better retrieval-augmented generation. LLMs aren't exposed to retrieval-augmented inputs during pretraining, limiting their ability to use retrieved text effectively. Fine-tuning the LLM and retrieval model together can improve performance without requiring extensive data processing. How it Works: Authors from Meta fine-tuned Llama 2 (65B parameters) and DRAGON+, a retriever, to create RA-DIT 65B. They fine-tuned Llama 2 on prompts with retrieved text and questions, and fine-tuned DRAGON+ to retrieve more relevant chunks. Fine-tuning was supervised for tasks like question-answering and self-supervised for text chunk completion. Results: RA-DIT 65B achieved 49.1% accuracy on average across four question datasets, outperforming LLaMA 2 65B with DRAGON+ (45.1%) and LLaMA 2 65B alone (32.9%). With five example inputs, RA-DIT 65B reached 51.8% accuracy. RA-DIT offers an efficient way to enhance LLM performance with RAG, making it a valuable technique for developers. Details: RA-DIT fine-tunes Llama 2 and DRAGON+ to work together effectively, leveraging the strengths of both models to generate better output. By fine-tuning the LLM to better use retrieved knowledge and the retrieval model to select more relevant text, RA-DIT achieves improved performance without requiring extensive data processing. https://lnkd.in/gf4fGVkC

Exciting breakthrough in Retrieval Augmented Generation (RAG): Researchers have developed GFM-RAG, the first Graph Foundation Model for enhancing LLM knowledge retrieval. >> Key Innovations Novel Architecture: GFM-RAG introduces a query-dependent Graph Neural Network that can process complex knowledge relationships in a single step, dramatically improving both efficiency and accuracy compared to traditional multi-step approaches. Under the Hood - Constructs a knowledge graph index from documents to capture relationships between information - Uses a 6-layer query-dependent message passing neural network with 512-dimensional hidden states - Implements DistMult message functions and sum aggregation for graph processing - Pre-trains on 60 knowledge graphs containing over 14M triples and 700k documents >> Performance Highlights The system achieves state-of-the-art results across multiple datasets, outperforming existing methods by significant margins: - Up to 19.8% improvement in retrieval accuracy - 10x faster processing compared to multi-step approaches - Demonstrates strong zero-shot generalization across 7 different domains >> Impact This breakthrough by researchers from Monash University, Nanjing University of Science and Technology, and Griffith University represents a significant step forward in making LLMs more knowledgeable and efficient. The system's ability to scale and transfer across domains makes it particularly valuable for real-world applications.

Day 16/30 of LLMs/SLMs - Retrieval-Augmented Generation (RAG) Large Language Models are powerful, but they have a fixed memory. They cannot know anything that happened after their training cut-off, and they struggle with facts that were never part of their dataset. When they lack the right information, they guess. The result is fluent but unreliable text — the hallmark of hallucination. Retrieval-Augmented Generation (RAG) fixes that by giving models a way to look up information before they answer. RAG is best understood as a three-stage pipeline, and LangChain has become the de facto standard framework for building each stage efficiently. 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠 You start by collecting and preparing your documents. LangChain’s loaders handle PDFs, web pages, CSVs, and APIs. These documents are then split into smaller, semantically meaningful chunks and converted into embeddings using models like OpenAI’s text-embedding-3-small, SentenceTransformers, or InstructorXL. Those embeddings are stored in a vector database such as FAISS, Pinecone, Weaviate, or Chroma, which lets you perform similarity search later. 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 When a query arrives, LangChain converts it into an embedding and searches the vector store for the most relevant documents. Retrieval strategies vary — basic similarity search, maximal marginal relevance (MMR) to diversify context, or hybrid retrieval that mixes semantic and keyword search. The retrieved text chunks are then added to the prompt as contextual grounding. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 The LLM receives the augmented prompt containing both the user query and retrieved passages. It synthesizes an answer based on that external knowledge. LangChain manages prompt templates, context formatting, and memory across queries, making the process modular and repeatable. 𝐖𝐡𝐲 𝐑𝐀𝐆 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 RAG fundamentally improves factual accuracy and trust. On benchmarks such as Natural Questions and TriviaQA, a base model like LLaMA 2-13B might achieve 45 F1, while RAG-augmented versions reach 65–70 F1 - matching much larger and costlier models. 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐫𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧 𝐑𝐀𝐆 If you want to experiment, LangChain makes it approachable. A minimal prototype takes fewer than 20 lines of code. Here’s a good progression 👉 Start with the LangChain tutorial: https://lnkd.in/gUpHpkKT 👉 Add a vector store: Try Chroma for local experiments or Pinecone for scalable hosting. 👉 Experiment with retrieval methods: compare similarity search vs. MMR. 👉 Integrate your own data: ingest PDFs, database exports, or web content. 👉 Deploy a chain: connect your retriever, model, and prompt template into a single workflow. Tune in tomorrow for more SLM/LLMs deep dives. -- 🚶➡️ To learn more about LLMs/SLMs, follow me - Karun! ♻️ Share so others can learn, and you can build your LinkedIn presence!