How to Apply Large Language Model Capabilities

“You don’t need to be a data scientist or a machine learning engineer- everyone can write a prompt” Google recently released a comprehensive guide on prompt engineering for Large Language Models (LLMs), specifically Gemini via Vertex AI. key takeaways for the article: What is prompt engineering really about? It’s the art (and science) of designing prompts that guide LLMs to produce the most accurate, useful outputs. It involves iterating, testing, refining not just throwing in a question and hoping for the best. Things you should know: 1. Prompt design matters. Not just what you say, but how you say it: wording, structure, examples, tone, and clarity all affect results. 2. LLM settings are critical: • Temperature = randomness. Lower means more focused, higher means more creative (but riskier). • Top-K / Top-P = how much the model “thinks outside the box.” • For balanced results: Temperature 0.2 / Top-P 0.95 / Top-K 30 is a solid start. • Prompting strategies that actually work: • Zero-shot, one-shot, few-shot • System / Context / Role prompting • Chain of Thought (reasoning step by step) • Tree of Thoughts (explore multiple paths) • ReAct (reasoning + external tools = power moves) 4. Use prompts for code too! Writing, translating, debugging, just test your output. 5. Best Practices Checklist: • Use relevant examples • Prefer instructions over restrictions • Be specific • Control token length • Use variables • Test different formats (Q&A, statements, structured outputs like JSON) • Document everything (settings, model version, results) Bottom line: Prompting is a strategic skill. If you’re building anything with AI, this is a must read👇

How to Build an AI Agent for Data Analysis: A Blueprint An "agent" is more than just a chatbot. It’s a system designed to understand a goal, create a plan, and use tools to actively accomplish that goal. You can build your own powerful agent for data analysis, transforming how users interact with their data. This blueprint outlines the core components required to turn simple questions into actionable insights. An agentic system is built on three foundational concepts: an LLM for reasoning, a set of tools for taking action, and a sophisticated memory for learning and context. 1. The LLM: Your Agent's Reasoning Core At the heart of any data analysis agent is its reasoning core: a Large Language Model (LLM) like OpenAI's GPT or Google's Gemini. To build this, create a central orchestrator service (e.g., a Chat Service). This service shouldn't just pass the user's question to the LLM. Instead, it should enrich the prompt with context from the agent's memory. The LLM's role is not merely to respond, but to create a step-by-step plan and generate the precise Python code needed to perform the analysis. 2. Tools: Give Your Agent Hands-on Capabilities An agent is only as good as the tools it can use. For a data analysis agent, the primary tool is the ability to execute code. After the LLM generates an analysis script, your orchestrator service must run it against the relevant dataset. This is the most critical agentic step: it moves the system from simply planning to actively doing. You can equip your agent with other tools, such as services for data loading, chart generation, or even calling external APIs, allowing it to handle a wide variety of analytical tasks. 3. Memory: Enable Context and Learning To elevate your agent from a one-shot tool to an intelligent partner, you need to implement memory. A robust approach is to use a graph database like Neo4j to manage two distinct types: ➜ Short-Term Memory: Implement a mechanism to track the current conversation history for each user session. This allows your agent to understand follow-up questions ("now show me that by region") and maintain context, just like a human analyst would. ➜ Long-Term Memory: This is where your agent can learn. Every time it successfully executes an analysis, store the user's query and the generated code as a "solution." By creating a vector embedding of the query, you can enable semantic search. When a new question comes in, the agent can first search its long-term memory for a similar problem it has already solved, allowing it to deliver accurate results faster and more efficiently over time. By integrating these three components, your application will function as a true AI agent. Your central orchestrator service will drive the powerful loop of Memory -> Reasoning -> Action, creating a system that doesn't just answer questions, but actively solves them.

Curious about how AI really works under the hood? You’ve seen the hype—ChatGPT, image generators, smart assistants—but how does it all actually come together? Let’s break it down. No jargon. No advanced degrees required. Here’s a beginner-to-builder roadmap for understanding Generative AI: 1. Start with the Basics Forget the buzzwords for a moment. Start by understanding: What’s the difference between AI, Machine Learning, and Deep Learning? How do models learn from data? Why linear algebra isn’t just complex math—it’s essential to how machines “think.” Tip: Matrix multiplication is key to how neural networks update and learn. 2. Data Preparation & Language Model Fundamentals Prepping data is foundational. It’s how you teach the model to read and understand. Clean your data: tokenization, removing stopwords Represent text as numbers: TF-IDF, Word2Vec, BERT embeddings Learn the basics of models like GPT and BERT Example: “The sky is blue.” → Tokenized as ['The', 'sky', 'is', 'blue'] 3. Fine-Tuning Large Language Models (LLMs) You don’t always start from scratch—use what’s already available. Load a pre-trained model Fine-tune it on your specific dataset Use libraries like Hugging Face Transformers, LoRA, and PEFT Example: Fine-tune GPT on customer support data to generate accurate, context-aware replies. 4. Multimodal Language Models Combine visual and language capabilities for more intelligent AI. Learn about CLIP, Flamingo, and Gemini-style models Enable applications like image captioning and AI assistants with visual input Build systems that can understand both text and images Example: Ask AI “What’s in this image?” and it can describe its content. 5. Prompt Engineering How you ask matters. Prompt design is a powerful skill. Explore zero-shot, few-shot, and chain-of-thought prompting Develop and test prompt templates Use frameworks like LangChain and PromptLayer for better results Example: Prompt—“Summarize this article in 3 bullet points.” → AI returns concise takeaways. 6. Retrieval-Augmented Generation (RAG) LLMs don’t know everything—and they forget facts. Integrate external data using vector databases like FAISS or Weaviate Enable your AI to retrieve accurate, real-time knowledge Build tools like a ChatGPT that reads and responds based on your PDFs or internal docs Example: AI reads your company docs to provide fact-based answers instead of guessing. Whether you're just getting started or aiming to build something real, this roadmap gives you the foundation to go from concepts to creation. Interested in resources or a hands-on crash course? Feel free to comment or reach out. #GenerativeAI #LLM #PromptEngineering #MachineLearning #DeepLearning #AIApplications #ArtificialIntelligence #DataScience #RAG #LangChain #HuggingFace