These days it seems like artificial intelligence is taking over the world. It almost feels like Bitcoin all over again… everyone is talking about it, not many people know how the magic works, and even fewer know how to make it work for them.
The thing is, the public tools are mostly one size fits all. They are widely trained on the open content of the internet. That’s not to say they aren’t doing some really amazing thing, however, in development and business the need for closed data and/or more structured processing of the LLM.
Hereβs a comprehensive list of AI models and architectures, including their pros, cons, and use cases.
1. Augmented Models (RAG, Tool-Using AI)
AI models that integrate external data sources, APIs, or tools to enhance responses dynamically.
β Pros:
- Access to real-time data and external knowledge.
- Reduces model hallucination by grounding responses.
- Enables integration with specialized tools (e.g., databases, search engines).
β Cons:
- Can introduce latency due to API calls.
- Requires robust orchestration for optimal efficiency.
- External sources must be reliable to maintain response quality.
π Use Cases:
- AI search assistants (fetching live data from databases).
- Automated research tools (retrieving case law, news, or academic papers).
- Dynamic chatbot responses (e.g., customer support integrating company databases).
2. Prompt Chaining (Sequential AI Processing)
A method where AI processes multiple prompts in sequence, refining its response through multiple steps.
β Pros:
- Improves complex reasoning by breaking problems into steps.
- Reduces prompt complexity by structuring workflows.
- Enables multi-turn conversational AI.
β Cons:
- Can increase response time due to multiple processing steps.
- Higher chance of error propagation across chained prompts.
- Requires careful prompt engineering for consistency.
π Use Cases:
- AI-driven writing assistants (drafting, revising, summarizing).
- Data processing pipelines (e.g., extracting structured info from documents).
- Multi-step problem solving (e.g., coding/debugging helpers).
3. Parallelization (Concurrent AI Processing)
Running multiple AI models or processes simultaneously to enhance efficiency.
β Pros:
- Faster processing compared to sequential models.
- Enables ensemble learning (multiple models improving accuracy).
- Efficient handling of multi-tasking AI applications.
β Cons:
- Requires effective synchronization to merge outputs properly.
- Increased computational demands.
- Complexity in orchestration and model coordination.
π Use Cases:
- Multimodal AI (text, image, video analysis combined).
- Real-time content generation (e.g., speech-to-text while translating).
- Fact-checking AI (cross-referencing multiple sources at once).
4. Routing Models (Dynamic AI Selection)
Routing models are used to determine which AI model or system should handle a specific request. Instead of relying on a single AI, a router directs input to the most suitable model.
β Pros:
- Increases efficiency by selecting the best model for a task.
- Allows for modular AI systems (different models for different use cases).
- Can combine generative and retrieval-based AI effectively.
β Cons:
- Requires a strong decision-making algorithm (e.g., classifiers, rule-based logic).
- Introduces complexity in system design.
- Model selection errors can impact performance.
π Use Cases:
- Hybrid AI assistants (GPT for text, Stable Diffusion for images).
- Customer support AI (routing issues to the right model or department).
- Multilingual AI processing (detects language and routes to the correct model).
5. Orchestrator-Worker Models (Task Automation & Distributed AI)
This model follows a master-worker approach, where an Orchestrator (controller) assigns tasks to Worker AIs, which process subtasks in parallel.
β Pros:
- Improves scalability (multiple worker AIs can process tasks at once).
- Supports task delegation and multi-step AI workflows.
- Enhances parallelization and efficiency.
β Cons:
- Requires effective task decomposition and reassembly.
- Increased computational cost for managing multiple worker AIs.
- Latency may occur if workers need to wait for dependencies.
π Use Cases:
- Large-scale document processing (e.g., AI parsing multiple PDF sections).
- Automated content moderation (worker AIs analyze text, images, videos).
- AI-powered project management (assigning subtasks to different AI workers).
6. Evaluator-Optimizer Models (Self-Improving AI)
This model structure involves an Evaluator AI that assesses output quality and an Optimizer AI that fine-tunes models dynamically.
β Pros:
- Enables self-improving AI systems.
- Reduces errors by evaluating responses before final output.
- Supports reinforcement learning & continuous improvement.
β Cons:
- Can slow down response times due to added evaluation steps.
- Requires a strong evaluation metric (subjective tasks are harder to optimize).
- Optimizer AI must be well-trained to avoid degrading model performance.
π Use Cases:
- AI code review & bug fixing (Evaluator detects issues, Optimizer refines).
- AI-powered writing assistants (Evaluator scores drafts, Optimizer improves).
- Self-learning AI chatbots (user feedback improves chatbot responses).
π₯ Which Model Should You Focus On?
With Routing, Orchestrator-Worker, and Evaluator-Optimizer models, AI systems can scale, optimize, and self-improveβessential for complex AI applications like LangGraph, AI pipelines, and automation workflows.
If you’re looking to focus on specific AI capabilities, hereβs a breakdown:
- Conversational AI β GPT, LLaMA (Decoder models).
- Search & classification β BERT, RoBERTa (Encoder models).
- Creative image generation β Stable Diffusion, DALLΒ·E.
- Real-time decision-making β Reinforcement Learning.
- Scalable AI automation β Orchestrator-Worker models.
- Self-improving AI systems β Evaluator-Optimizer models.
- Hybrid AI systems β Routing + Augmented AI models.
- Multimodal AI β Hybrid models combining multiple approaches.