POW

Beyond GenAI

The wider world of
neural networks.

Not every AI system should start with an LLM. Dense networks, CNNs, transformers, and autoencoders each solve different classes of problems with different tradeoffs in cost, latency, and control.

Foundations

Dense networks and MLPs

Use these to introduce compact learnable systems before readers jump to transformers. They are a simple baseline for many structured tasks.

Dense networks and multilayer perceptrons are often the cleanest starting point for tabular data, scoring systems, forecasting features, and other structured inputs. They are easier to train, faster to serve, and easier to debug than larger architectures when the data does not have strong spatial or sequential structure.

  • Strong baseline for tabular and structured inputs
  • Lower latency and simpler deployment
  • Easier to inspect than larger sequence models
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Perception

CNNs and perception models

Explain how convolutional models still matter for image and signal tasks, especially where data has strong local structure.

Convolutional networks still shine when nearby patterns matter most, such as images, audio features, medical imaging, industrial vision, and sensor grids. Their inductive bias makes them extremely efficient for detecting edges, textures, and repeating motifs without paying the full cost of a general-purpose sequence model.

  • Excellent for images, audio features, and sensor maps
  • Efficient when local structure matters most
  • Still highly relevant for embedded perception systems
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Sequences

Transformers

Place LLMs inside the wider transformer story, including text, speech, and multimodal systems rather than treating chat as the only endpoint.

Transformers are a broad architecture family for sequence modeling, not just chatbots. LLMs are one important branch, but the same core ideas power retrieval-aware text systems, speech models, vision transformers, and multimodal stacks that reason across text, audio, and images.

  • LLMs are one part of a wider transformer family
  • Useful across text, speech, vision, and multimodal tasks
  • Best when context handling and flexible sequence modeling matter
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Latent Systems

Autoencoders and embedding models

Use these to show that representation learning, compression, and anomaly detection are often better served by smaller purpose-built architectures.

These models are often the right choice when the product needs compact representations instead of free-form generation. They are especially valuable for compression, denoising, semantic similarity, clustering, retrieval features, and anomaly detection, where a purpose-built latent space is more useful than an LLM response.

  • Great for embeddings, compression, and denoising
  • Often better for anomaly detection than generative models
  • Useful when the goal is a strong latent space, not text output
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Decision Framework

How to choose the right neural architecture.

The goal is not to force every product through one model class. Good systems start by matching the architecture to the structure of the data and the shape of the task.

Start with the narrowest architecture that fits the data

If the input is structured, local, or stable, a smaller network family usually gives you better cost, latency, and operational clarity than jumping straight to a transformer.

Use transformers when context and modality breadth really matter

Transformers earn their complexity when long-range dependencies, flexible sequence modeling, or multimodal behavior are central to the product rather than optional extras.

Treat representation learning as a first-class product primitive

Autoencoders and embedding models are often the right foundation when the goal is retrieval, clustering, monitoring, compression, or anomaly detection instead of language generation.

Teach architecture choice, not just model choice

A strong AI system starts with the right problem framing. The most useful lesson is often whether you need an LLM at all, not which LLM to pick.

Ask the AI for help