Table of Contents

1. Introduction to Generative AI
2. Popular Generative AI Tools
   • Text Generation Tools
   • Image Generation Tools
   • Video Generation Tools
   • Audio Generation Tools
   • Code Generation Tools
   • Local/Self-Hosted AI Tools
3. Essential Concepts and Definitions
4. Prompt Engineering
   • Core Principles
   • Essential Techniques
   • Advanced Strategies
   • Best Practices for 2025
5. Advanced AI Architectures
6. Integration and Implementation
7. Resources and Further Learning

Local/Self-Hosted Considerations

Local LLMs offer cost-effective and secure alternatives to cloud-based options. By running models on your own hardware, you can avoid recurring API costs and keep sensitive data within your infrastructure.

Key Benefits:

• Privacy: Complete data privacy with prompts and data never leaving your device
• Cost: No subscription costs, allowing unlimited AI usage without fees
• Offline Operation: Work without internet connectivity, ideal for remote work or air-gapped environments
• Customization: Fine-tune models and inject domain knowledge like GDPR or medical terminology
• Performance: Reduced latency by eliminating network delays for faster responses

Popular Local Tools: Ollama, LM Studio, LocalAI, GPT4All, and AnythingLLM are among the top solutions for 2025, each offering different interfaces from command-line to polished desktop applications.

Introduction to Generative AI

Generative Artificial Intelligence (Generative AI) refers to AI systems that can create new content and ideas, including conversations, stories, images, videos, music, and code. Unlike traditional AI that analyzes existing data, generative AI produces original outputs by learning patterns from vast datasets and generating new content that follows similar patterns.

Key Characteristics:

• Creative: Produces original content across multiple modalities
• Adaptive: Learns from examples and adjusts to specific requirements
• Scalable: Can generate large volumes of content efficiently
• Interactive: Responds to user inputs and iterates based on feedback

Popular Generative AI Tools

Text Generation Tools

ChatGPT (OpenAI)

• Description: Leading conversational AI model with natural language understanding and generation capabilities
• Strengths: Versatile applications from customer service to content creation, coding assistance, and complex reasoning
• Latest Updates (2025): Enhanced multimodal capabilities supporting text, images, audio, and video
• Best for: General-purpose conversational AI, content creation, coding assistance, and analysis

Claude (Anthropic)

• Description: Advanced AI assistant focusing on helpful, harmless, and honest interactions
• Strengths: Excellent for detailed analysis, writing assistance, and complex reasoning tasks
• Unique Features: Strong emphasis on safety and constitutional AI principles
• Best for: Research, analysis, creative writing, and technical documentation

Gemini (Google - formerly Bard)

• Description: Google’s AI assistant with strong integration to Google Search and services
• Strengths: Access to current information, multimodal capabilities, and Google ecosystem integration
• Latest Updates (2025): Expanded AI Mode in Search, Deep Research capabilities, and enhanced learning tools for students
• Best for: Research, current information retrieval, and Google Workspace integration

DeepSeek

• Description: Cost-efficient AI model that matches leading capabilities at a fraction of development cost (~$6 million vs $100 million)
• Best for: Technical writing, coding, and cost-conscious applications

Jasper

• Description: AI content generation platform tailored for marketers and business teams with “Brand Voice” feature
• Strengths: Marketing-focused templates, brand consistency, enterprise features
• Best for: Marketing content, brand-specific writing, enterprise content at scale

Image Generation Tools

DALL-E 3 (OpenAI)

• Description: Leading text-to-image AI generator integrated with ChatGPT
• Strengths: Now reliably generates text within images, addressing previous limitations
• Best for: Blog graphics, social media content, and detailed image prompts

Midjourney

• Description: Often considered the OG of AI image generation, favored for its painterly aesthetic
• Access: Discord-based interface
• Best for: Artistic images, creative visuals, and source images for video generation

Leonardo AI

• Description: AI art generator offering precise control over image generation
• Strengths: Advanced customization options, style controls
• Best for: Professional design work, detailed artistic control

Adobe Firefly (Integrated with Photoshop)

• Description: AI features integrated into Creative Cloud suite, part of $52.99/month All Apps plan
• Strengths: Professional integration, established workflows
• Best for: Professional graphic design, photo editing workflows

Video Generation Tools

Synthesia

• Description: AI-powered platform for creating videos with AI avatars
• Strengths: Enterprise focus with AWS partnership and ISO/IEC 27001:2022 certification
• Best for: Corporate training, marketing videos, multilingual content

Runway ML

• Description: AI-powered creative media platform with Gen-4 technology for world-consistent video generation
• Funding: Raised $308 million in Series D, reaching over $3 billion valuation
• Best for: Creative video editing, text-to-video generation, professional workflows

Google Veo 2

• Description: Next-generation video synthesis from Google DeepMind
• Best for: High-quality video generation, Google ecosystem integration

Audio Generation Tools

ElevenLabs

• Description: Advanced AI voice generation and cloning platform
• Strengths: High-quality voice synthesis, voice cloning capabilities
• Best for: Voiceovers, audiobooks, multilingual content

Suno

• Description: AI music generation platform that creates songs with lyrics, musical compositions, and vocals from simple prompts
• Pricing: Free plan with 50 daily credits; Pro plan $10/month for 2,500 monthly credits (~500 songs)
• Best for: Music creation, background audio, creative projects

Code Generation Tools

GitHub Copilot

• Description: AI coding assistant providing real-time coding assistance, integrated with version control and CI/CD
• Best for: Software development, code completion, debugging assistance

Cursor

• Description: AI-powered code editor with advanced context awareness
• Best for: Full-stack development, code refactoring, AI-assisted programming

Local/Self-Hosted AI Tools

Local AI tools offer significant advantages including complete data privacy, no subscription costs, offline operation, customization control, and reduced latency. In 2025, running LLMs locally has become increasingly practical for both individual developers and organizations.

Why Choose Local AI?

Privacy & Security: Your data never leaves your device, making it perfect for sensitive projects Cost Effectiveness: Avoid recurring subscription fees and API costs, with potential long-term savings for high-volume usage Offline Capability: Work without internet connectivity, ideal for remote work or air-gapped environments Customization: Fine-tune models for specific tasks, inject domain knowledge, and control every aspect of the deployment Performance: Eliminate network delays and avoid rate limits

Text Generation (Local LLMs)

Ollama

• Description: Open-source tool that downloads, manages, and runs LLMs directly on your computer in isolated environments
• Platforms: macOS, Linux, Windows
• Popular Models: Llama 3 (8B for mid-range, 70B for powerful hardware), Phi-3 (optimized for 8GB RAM), Code Llama for programming
• Interface: Command-line based, can be paired with OpenWebUI for graphical interface
• Best for: Users comfortable with command line, homelab enthusiasts, developers

LM Studio

• Description: Most polished graphical user interface for managing and running local LLMs, accessible for non-technical users
• Strengths: User-friendly GUI, extensive model library, fine-tuning capabilities
• Best for: Users who prefer graphical interfaces over command-line tools

Jan

• Description: Comprehensive ChatGPT alternative that runs completely offline, offering full control and privacy
• Features: Cross-platform support, works across multiple hardware configurations
• Best for: Users looking for a polished, all-in-one solution

GPT4All

• Description: Polished desktop application with minimal setup required
• Platform: Particularly strong on Windows
• Best for: Windows users who prefer traditional desktop applications

Text-Generation-WebUI

• Description: Feature-rich interface with easy installation and flexibility for various model formats
• Strengths: Web interface, comprehensive features, supports multiple model formats
• Best for: Users wanting powerful features with web-based access

LocalAI

• Description: Most versatile platform for developers, offering OpenAI API compatibility
• Features: Supports diverse model types (text, image, audio), Docker support, API compatibility
• Docker Setup: Simple deployment across different systems
• Best for: Developers needing flexible, API-compatible local LLM hosting

AnythingLLM

• Description: Open-source AI application with desktop focus, featuring React interface, NodeJS server, and document processing
• Strengths: Document chat, AI agents, local data processing, multi-user Docker support
• Best for: Teams needing document analysis and AI agents while maintaining data privacy

Recommended Local Models for 2025

Meta Llama 3 Family

• Llama 3 8B: Works on mid-range machines (16GB RAM), excellent for general tasks
• Llama 3 70B: Requires powerful hardware but delivers near-commercial quality results
• Strengths: Excellent balance of performance and efficiency, strong reasoning capabilities

Microsoft Phi-3

• Description: Lightweight model optimized for lower-end systems (8GB RAM), great for coding and reasoning
• Best for: Resource-constrained environments, quick responses

DeepSeek Coder

• Description: Best balance of speed and autocomplete accuracy, responds in <80ms for VS Code
• Strengths: Programming tasks, code completion, technical documentation
• Best for: Developers needing fast, accurate code assistance

Mistral Models

• Description: European-developed models offering strong performance
• Variants: 7B model requires 8-12GB VRAM
• Best for: Code generation, general text tasks

Local Image Generation

Stable Diffusion (Local Setup)

• Models Available: SD 1.4, 1.5, 2.0, 3.5 (Medium, Large, Turbo), SDXL, SDXL Turbo
• Hardware Requirements: Minimum 6GB VRAM recommended for SDXL, preferably 10GB
• Interface Options: Web UIs like AUTOMATIC1111, ComfyUI, or InvokeAI
• Customization: Extensive community checkpoints available on CivitAI, fine-tuning capabilities

LocalAI Image Generation

• Backend: Diffusers backend supporting Stable Diffusion and other models
• Setup: API-compatible image generation with local models
• Features: Text-to-image, image-to-video, and video generation capabilities

Hardware Requirements

Text Models

• 7B Models: 8-12GB VRAM (RTX 3080/4070 level)
• 13B Models: 16-20GB VRAM
• 70B Models: Can be quantized to fit 24GB (RTX 4090) using q4_k_m format

Image Models

• SDXL Models: 6GB VRAM minimum, 10GB+ recommended
• CPU Fallback: Models can run on CPU but generate very slowly

Setup and Deployment

Quick Start Options

• Docker Containers: Use pre-built Docker Compose setups like n8n Self-Hosted AI Starter Kit
• One-Click Installs: Tools like Ollama offer simple installation with automatic model downloads
• Cloud VPS: Rent GPU-enabled virtual servers for more powerful models

Integration Frameworks

• n8n + Ollama: Low-code workflows with LangChain integration for complex AI applications
• API Compatibility: Many tools offer OpenAI-compatible APIs for easy integration

Use Cases for Local AI

Enterprise Applications

• GDPR Compliance: Keep all personal data on-premises for EU regulatory compliance
• Healthcare/Legal: Sensitive data processing without cloud exposure
• Internal RAG Systems: Train on proprietary knowledge bases securely

Development Workflows

• Code Assistance: Eliminate rate_limit_exceeded errors, define your own queue behavior
• Content Creation: Generate text, images, and audio without usage limits
• Prototyping: Rapid iteration without API costs

Essential Concepts and Definitions

Agentic AI

AI systems that can accomplish specific goals with limited supervision, consisting of AI agents that mimic human decision-making to solve problems in real time. In 2025, 88% of executives plan to increase AI-related budgets specifically because of agentic AI’s potential.

Examples: Smart personal assistants, autonomous vehicles, cybersecurity systems, healthcare management tools

Natural Language Processing (NLP)

Machine learning technology that enables computers to interpret, manipulate, and comprehend human language, forming the foundation for most generative AI text applications.

Tokens

The fundamental units of text that AI models use to process and understand language. These can be words, subwords, or individual characters, depending on the tokenization method. Understanding tokens is crucial for managing costs and context limits.

Large Language Models (LLMs)

AI models trained on vast amounts of text data to understand and generate human-like language. Examples include GPT-4, Claude, and Gemini.

Transformers

Revolutionary architecture introduced by Vaswani et al. in “Attention Is All You Need” (Google, 2017). This paper fundamentally changed NLP and enabled the current AI revolution.

The underlying neural network architecture that enables modern generative AI models to handle large amounts of data efficiently and generate complex outputs. The “T” in GPT stands for Transformer.

Key innovation: The attention mechanism allows the model to focus on relevant parts of input when generating each output, similar to how humans focus on important words when understanding a sentence.

Why transformers revolutionized AI:

• Parallelization: Unlike RNNs, can process entire sequences simultaneously (enabling GPU acceleration)
• Long-range dependencies: Captures relationships between distant words effectively
• Scalability: Performance improves with more data and compute (no apparent ceiling yet)
• Transfer learning: Pre-trained transformers can be fine-tuned for specific tasks

Architecture components:

• Self-attention layers: Allow each position to attend to all positions
• Feed-forward networks: Process attention outputs
• Positional encoding: Provides sequence order information
• Multi-head attention: Multiple attention mechanisms in parallel

Impact: Enabled GPT series, BERT, Claude, Gemini, and virtually all modern LLMs. Estimated to save billions in research costs by providing a proven, scalable architecture.

Hallucination

When AI models generate information that appears plausible but is actually false or fabricated. This is a key limitation to understand when working with generative AI.

Fine-tuning

The process of adapting a pre-trained AI model for specific tasks or domains by training it on specialized datasets.

Context Window

The amount of text (measured in tokens) that an AI model can consider at once when generating responses. Larger context windows allow for more complex conversations and document analysis.

Prompt Engineering

Prompt engineering is the practice of crafting inputs that guide AI models to generate desired outputs. In 2025, prompt engineering has evolved beyond simple tricks to encompass formatting techniques, reasoning scaffolds, role assignments, and security considerations.

Core Principles

1. Give Direction

Describe the desired style in detail or reference a relevant persona.

Examples:

• “As a professional financial analyst who must follow regulatory compliance…”
• “In the style of a technical documentation writer, explain…”
• “Acting as an experienced marketing strategist…”

2. Specify Format

Define rules to follow and the required structure of the response.

Examples:

• “Respond as a JSON object with the following fields…”
• “Format as a bullet-point list with exactly 5 items”
• “Structure as: Problem → Analysis → Recommendation”

3. Provide Examples

Insert diverse test cases showing the task done correctly (few-shot prompting).

4. Evaluate Quality

Test prompts multiple times to ensure consistent, high-quality results.

5. Divide Labor

Split complex tasks into multiple steps or use chained prompts.

Essential Techniques

Zero-shot Prompting

Technique where a language model performs a task without any examples, using clear, concise instructions.

Example:

"Summarize this article in 3 bullet points focusing on the main findings."

Few-shot Prompting

Technique where examples are included in the prompt to facilitate learning, particularly useful for complex tasks.

Example:

Grade these student responses following my style:

Homework: "The old clock tower chimed loudly, echoing through the deserted village square."
Grade: A
Reason: Well-written sentence with good imagery.

Homework: "She found a hidden note iside the ancient book."
Grade: B
Reason: Good concept, minor spelling error.

Now grade: "The garden was filled with beautiful flowers and trees."

Chain-of-Thought Prompting (CoT)

Technique that encourages AI to articulate its thought process step-by-step, particularly effective for complex problem-solving tasks.

Example:

"Solve this step by step, showing your reasoning:
If a store offers 25% off and an additional 10% off the discounted price, what's the final discount on a $100 item?"

Advanced Strategies

Persona-Based Prompting

Assigning roles or personas to AI leads to more tailored, context-specific responses by providing a frame of reference for the model.

Example:

"You are a senior data scientist with 10 years of experience in healthcare analytics. 
Explain the concept of statistical significance to a hospital administrator 
who needs to understand clinical trial results."

Task Decomposition

Breaking complex tasks into smaller, manageable subtasks.

Example:

"I need help preparing a business presentation. Let's work through this step by step:
1. First, help me outline the key sections
2. Then we'll develop content for each section
3. Finally, we'll refine the messaging for the audience"

Self-Consistency Prompting

Technique involving sampling multiple outputs and comparing reasoning paths to identify common patterns, useful for complex reasoning tasks.

Prompt Scaffolding

Defensive prompting technique that wraps user inputs in structured templates to limit the model’s ability to misbehave, even with adversarial input.

Best Practices for 2025

Based on current research, here are key insights for effective prompt engineering:

1. Structure Over Cleverness: Clear structure and context matter more than clever wording
2. Model-Specific Optimization: Different models (GPT-4, Claude, Gemini) respond better to different formatting patterns
3. Iterative Refinement: Adopt an experimental approach to test various phrasing, layouts, and contextual cues
4. Security Awareness: Prompt engineering can be a security risk when exploited through adversarial techniques

Key Parameters to Understand

• Temperature: Controls randomness in output. Higher temperature = more creative but less consistent. Use temperature 0 for factual tasks
• Context Window: Amount of text the model can process at once
• System Prompts: Background instructions that shape the AI’s behavior throughout the conversation

Advanced AI Architectures

Retrieval-Augmented Generation (RAG)

RAG combines generative AI with external knowledge sources to provide more accurate, up-to-date information. K2View leverages RAG in their GenAI Data Fusion solution to ensure LLM prompts are grounded in enterprise data.

Key Components:

• Retrieval System: Searches relevant documents or data
• Context Integration: Combines retrieved information with user queries
• Generation: Produces responses based on both training data and retrieved context

Benefits:

• Reduces hallucination
• Provides current information beyond training cutoff
• Enables domain-specific expertise

Model Context Protocol (MCP)

MCP is an open protocol that standardizes how applications provide context to LLMs, helping build agents and complex workflows by connecting AI models to different data sources and tools.

Architecture:

• Client-server model where host applications connect to multiple servers
• Enables LLMs to access external data and tools securely
• Provides flexibility to switch between LLM providers

Benefits:

• Pre-built integrations for common data sources
• Standardized approach to AI tool integration
• Enhanced security for enterprise data

Multi-Agent Systems

Systems where multiple AI agents work together to accomplish complex tasks, each specializing in different aspects of the overall goal.

Integration and Implementation

Enterprise Considerations

73% of executives believe AI agents will provide significant competitive advantage, with companies prioritizing responsible AI practices for better ROI.

Key Areas:

• Trust and Safety: Implementing responsible AI practices from the start
• Data Infrastructure: Ensuring AI tools can access and learn from the right data
• Capability Building: Upskilling teams in AI fluency
• Compliance: Meeting regulatory requirements and industry standards

Cost Optimization

Great prompt engineering can reduce costs while improving performance by making AI interactions more efficient.

Strategies:

• Optimize prompt length and structure
• Use appropriate model sizes for tasks
• Implement caching for repeated queries
• Monitor and analyze usage patterns

Performance Monitoring

• Track output quality and consistency
• Monitor for bias and fairness issues
• Measure user satisfaction and task completion
• Analyze cost per successful interaction

Quick Reference

Tool Selection by Use Case

Prompt Engineering Quick Tips

1. Be Specific: Clear instructions beat clever wording
2. Provide Context: Give the AI role and background information
3. Show Examples: Few-shot prompting improves consistency
4. Structure Output: Define the format you need
5. Iterate: Test and refine prompts based on results

Local AI Hardware Requirements