What this lesson solves

The first three lessons focused on concepts. This lesson moves into implementation. If you want to build an AI × Web3 product or research workflow, you need a practical map of the toolchain instead of a long list of brand names.

A useful four-layer tool map

1. Model services

This layer provides inference capability. It is what actually produces outputs such as:

• text generation
• embeddings
• image generation
• speech recognition
• multimodal understanding

This layer affects cost, latency, context size, and model availability.

2. Application frameworks

This layer helps connect model capability to a real product instead of hand-writing every low-level interaction.

Common responsibilities include:

• prompt templating
• multi-step orchestration
• tool calling
• memory management
• retrieval-augmented generation
• agent execution flow

3. Data and vector systems

This layer organizes knowledge, documents, and historical context so the model is not limited to one input at a time.

Typical use cases include:

• storing document chunks
• semantic search
• managing conversation context
• organizing labeled summaries of onchain activity

4. Evaluation and observability

This layer tells you whether the system is actually working in production.

It usually covers:

• output quality checks
• cost monitoring
• latency monitoring
• error tracing
• prompt and version tracking

The most important capabilities for AI × Web3

If the goal is a real product rather than a short demo, the usual order of importance is:

1. model access and cost control
2. retrieval and context management
3. tool calling and agent execution
4. evaluation and replay
5. connection to onchain data or wallet systems

A minimal viable stack

If you want to build an early prototype, a minimal combination is often enough:

• one model service for understanding and generation
• one backend application for APIs, permissions, and logs
• one retrieval or vector layer for knowledge access
• one onchain data source
• one frontend interface

That stack is already enough for products such as:

• an onchain research assistant
• a governance proposal summarizer
• a wallet behavior analysis tool
• an agent risk dashboard

Do not choose tools only because they are new

Tool selection should be filtered through four criteria:

Reliability

Can the system run consistently, and is failure easy to debug?

Replaceability

Can you switch models or providers later without rebuilding the entire product?

Cost structure

Are inference, storage, and usage costs predictable enough for the product?

Fit for the scenario

Research products, execution products, education products, and consumer tools do not need the same stack.

Extra constraints in Web3

Once AI touches onchain systems, tool selection has to include three additional checks:

• Are permissions explicit?
• Is the data source trustworthy?
• Can the system fail safely?

That is why an AI × Web3 stack is not just “model plus wallet.” Execution boundaries need to be designed upfront.

Minimum takeaway

After this lesson, you should be able to explain:

• the main layers of an AI toolchain
• what model services, frameworks, data systems, and observability tools each do
• what a minimal viable AI × Web3 stack looks like
• why tool selection must consider cost, replaceability, and risk instead of only feature lists

Part 1 recap

By the end of Part 1, you should have a coherent line of thought:

• first understand the basic boundary of AI
• then understand how learning works
• then connect Deep Learning to modern large models
• finally map those capabilities onto a real toolchain

The next part moves into Web3 foundations and places those AI capabilities inside an onchain environment.

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