Question 1

Does Context Engineering still matter with the arrival of MCP?

Accepted Answer

Yes. MCP standardizes how AI tools connect to data sources and tools, but it does not decide which information is relevant. Context Engineering focuses on selecting, structuring, and delivering the right content to models, which remains essential and becomes even more valuable as MCP adoption grows.

Question 2

What happens when context windows get much larger?

Accepted Answer

Larger context windows theoretically reduce hallucinations by allowing models to see more information at once, but in practice they are expensive to compute and often still miss or skim important details in the middle of long inputs. Effective retrieval and context structuring remain necessary even with very large context windows.

Question 3

Will Context Engineering stay relevant as AI improves?

Accepted Answer

Yes. No matter how capable models become, it is neither practical nor cost-effective to load all possible information into every prompt. Context Engineering provides precise, task-specific information, which is critical in high-stakes domains like healthcare and law.

Question 4

Why didn’t the big AI labs solve efficient, context-rich chunking?

Accepted Answer

Major AI providers earn revenue from token usage and currently rely on rough chunking strategies, so highly efficient chunking would reduce their token consumption. In addition, building robust, structure-aware chunking is technically hard and competes with many other priorities, whereas POMA AI focuses on this problem exclusively.

Question 5

What’s the difference between POMA AI and Unstructured.io?

Accepted Answer

Unstructured.io is primarily an element extractor that converts document elements like tables and images into LLM-digestible text, with chunking as an additional feature. POMA AI focuses on full-document, structure-aware chunking to create coherent, hierarchy-preserving chunks optimized for RAG pipelines.