Obot Learning Center – Page 3
Working with Gemini API: Text Gen, Doc Processing & Code Execution
What Is the Google Gemini API? Google Gemini is a multi-modal large language model (LLM). It provides natural language and image processing capabilities to enable text generation, sentiment analysis, document processing, image and video analysis, and more. Using the Gemini API, developers can integrate AI functionalities into their applications without needing deep expertise in machine […]
Top 10 RAG Tutorials in 2024 + Bonus LangChain Tutorial
What Is Retrieval Augmented Generation (RAG)? Retrieval-augmented generation (RAG) combines large language models (LLMs) with external knowledge retrieval. Traditional LLMs generate responses based solely on pre-trained data. With RAG, the model can access updated and specific information at the time of inference, providing more accurate and context-rich responses. This method leverages repositories of external data, […]
8 Prompt Engineering Examples for Common NLP Tasks
What Is Prompt Engineering? Prompt engineering involves crafting specific instructions or queries for large language models (LLMs) to get desired outputs. It plays a crucial role in fine-tuning the capabilities of models like OpenAI’s GPT-4o, Google Gemini, and Anthropic Claude, ensuring they deliver accurate, relevant, and contextually appropriate responses. Instead of merely providing a broad […]
What Are AI Agents? A Complete Guide
What Are AI Agents? AI agents are software entities that perform tasks autonomously. They make decisions based on predefined rules, machine learning models, or a blend of both. Their design centers around achieving specific goals without constant human intervention. These agents can range from simple mechanisms executing repetitive tasks to complex systems navigating dynamic environments […]
Intelligent Automation: Pros/Cons, Use Cases & 5 Key Capabilities
What Is Intelligent Automation (IA)? Intelligent automation (IA) is the integration of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) to create systems that can perform both routine and complex tasks. These systems improve over time by learning from data and user interactions, leading to increased efficiency and decision-making capabilities. By using […]
Prompt Engineering in ChatGPT: 9 Proven Techniques
What Is Prompt Engineering in ChatGPT? Prompt engineering involves crafting inputs (prompts) that guide a large language model (LLM) to generate desired outputs. It’s a skill that combines understanding of natural language processing, creativity, and strategic thinking to communicate with the model. Prompt engineering can transform vague requests into precise commands that the AI interprets […]
LLM Application Development: Tutorial & 7 Steps to Production Apps
LLM application development involves creating software applications that leverage LLMs like OpenAI GPT or Meta LLaMA to generate, or manipulate natural language.
MCP Call Filtering: Stopping Prompt Injection and Securing Enterprise AI
As enterprises adopt Model Context Protocol (MCP) to connect AI agents and tools with internal systems, one of the biggest risks they face is untrusted or unsafe tool calls. Without safeguards, a malicious prompt, injected instruction, or poorly validated request could trigger dangerous behavior—such as exposing sensitive data, running unauthorized actions, or even spreading malware. […]
LLM Security: Top 10 Risks, Impact, and Defensive Measures
What Is LLM Security? LLM security focuses on safeguarding large language models against various threats that can compromise their functionality, integrity, and the data they process. This involves implementing measures to protect the model itself, the data it uses, and the infrastructure supporting it. The goal is to ensure that these models operate as intended […]
RAG vs. LLM Fine-Tuning: 4 Key Differences and How to Choose
What Is RAG (Retrieval-Augmented Generation)? Retrieval-Augmented Generation (RAG) merges large language models (LLMs), typically based on the Transformer deep learning architecture, with retrieval systems to enhance the model’s output quality. RAG operates by fetching relevant information from large collections of texts (e.g., Wikipedia, a search engine index, or a proprietary dataset) and fuses this external […]
Prompt Engineering: Techniques, Uses & Advanced Approaches
Explore the essentials of prompt engineering to optimize interactions with AI and improve output quality from language models.
Understanding RAG: 6 Steps of Retrieval Augmented Generation (RAG)
What Is Retrieval Augmented Generation (RAG)? Retrieval Augmented Generation (RAG) is a machine learning technique that combines the power of retrieval-based methods with generative models. It is particularly used in Natural Language Processing (NLP) to enhance the capabilities of large language models (LLMs). RAG works by fetching relevant documents or data snippets in response to […]