The 5 Concepts Every Developer Should Understand Before Building AI Features

2026-05-11 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, medium

Summary

This article outlines five fundamental concepts crucial for developers building robust AI features: tokens, embeddings, context windows, semantic vs. keyword search, and approximate vs. exact search. Tokens represent the units of text AI models process, with models having specific token limits that necessitate careful data pipeline design and chunking strategies. Embeddings convert text into numerical vectors representing meaning, enabling semantic search and recommendations, but are model-specific and require reindexing if models change. Context windows define the maximum input size for large language models, requiring strategies like chunking, RAG, or summarization for longer inputs. The article also differentiates between keyword search, which is exact and fast, and semantic search, which matches conceptual similarity, advocating for hybrid approaches in most production systems. Finally, it explains approximate nearest neighbor (ANN) search for vectors, which prioritizes speed over perfect recall, noting its implications for safety-critical or compliance-driven applications.

Key takeaway

For AI Engineers designing new application features, understanding these five core concepts is critical for production readiness. You should proactively design data pipelines around token limits and context windows, treat embedding model choices as schema decisions, and plan for reindexing. Implement hybrid search for most user-facing applications and carefully consider the trade-offs between approximate and exact vector search based on your application's specific requirements for recall and speed.

Key insights

Understanding five core AI concepts is crucial for building production-ready AI features that withstand real-world usage.

Principles

• AI features require robust data pipeline design.
• Model-specific data structures necessitate reindexing plans.
• Hybrid search combines precision with conceptual understanding.

Method

Design data pipelines around token limits and context windows. Plan for embedding model changes and reindexing. Combine semantic and keyword search for comprehensive results. Evaluate approximate vs. exact search based on application needs.

In practice

• Check model token limits for data chunking.
• Document embedding model versions.
• Implement hybrid search for user queries.

Topics

• AI Feature Development
• Tokens
• Embeddings
• Context Windows
• Semantic Search

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.