AI Technology stack

AI Technology Stack Explained: The Layers Making AI Work

Level 1: Data Layer

The foundation of every AI system.

• Components
• Text
• Images
• Audio
• Video
• Documents
• Databases
• Sensors
• IoT devices

Purpose

• Collect information for training and inference.

Examples

• Customer support tickets
• PDFs
• Product catalogues
• Medical records

Level 2: Data Processing Layer

Raw data must be cleaned and prepared.

Tasks

• Remove duplicates
• Handle missing values
• Convert formats
• Tokenisation
• Feature extraction
• Normalisation

Output

Clean, structured data suitable for AI models.

Level 3: Machine Learning Layer

The system learns patterns from historical data.

Types

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Deep Learning

Algorithms

• Decision Trees
• Random Forest
• Neural Networks
• Support Vector Machines
• Gradient Boosting

Level 4: Embedding Layer

This is one of the most important layers in Generative AI.

An embedding converts text, images, or audio into numerical vectors that capture semantic meaning.

Example: "Cat" -> [0.82, -0.21, 0.56, ...]

Similar concepts produce similar vectors.

Cat

Dog

Tiger

Lion

These vectors are close together because they are semantically related.

More on Semantic Search
More on Embedding

Level 5: Vector Database Layer

Embeddings are stored inside vector databases.

Examples:

• Pinecone
• Milvus
• Weaviate
• Qdrant
• Chroma
• pgvector

Responsibilities

• Store vectors
• Perform similarity search
• Retrieve relevant knowledge
• Support Retrieval-Augmented Generation (RAG)

More on RAG
More on Vector Database

Level 6: Foundation Model / Large Language Model (LLM)

This is the "brain" of many AI applications.

Examples:

• GPT
• Claude
• Gemini
• Llama
• Mistral

The model:

• Understands prompts
• Interprets context
• Predicts the next token
• Generates responses

Level 7: Reasoning Layer

The AI combines:

• User query
• Retrieved knowledge
• Internal reasoning
• Instructions

This layer helps:

• Solve problems
• Summarise information
• Write code
• Explain concepts
• Make recommendations

Level 8: Application Layer

This is what users interact with.

Examples:

• Chatbots
• AI search
• Customer support
• Recommendation engines
• AI assistants
• Translation services
• Image generation
• Voice assistants

Level 9: Feedback & Improvement Layer

Modern AI systems improve through:

• User feedback
• Human review
• Fine-tuning
• Reinforcement learning
• Continuous model updates
• Performance monitoring