In the previous series, we built the “Brain” (Agents) and the “Body” (Orchestration). Now, we must feed it.
The biggest problem in AI right now isn’t the model; it’s the Memory. Startups are dumping terabytes of PDFs and code into Vector Databases and hoping for magic. They believe “Semantic Search” is a silver bullet. But when they ask: “How do I fix Error 500?”, the AI retrieves a generic document about “Server Maintenance” instead of the specific “Error 500” log.
Vector Search (Dense Retrieval) is great for concepts (“Safety”), but terrible for specifics (“Error #9981”). Most teams are doing “Flat Indexing”-chopping documents into 512-token chunks and treating them all as equal. This is naive.
As someone who built the Code Search platform for Azure DevOps, I can tell you: Not all tokens are created equal. We need to stop doing “Data Dumping” and start doing Context Engineering.
1. The “Flat Chunk” Fallacy (Structure-Aware Indexing)
The Naive Approach:
“Split the document every 500 words. Embed it. Store it.”
The Engineering Reality:
This flattens the world. It assumes a random paragraph on page 50 has the same information density as the Executive Summary on page 1. When we built Code Search, we didn’t just index text. We parsed the Abstract Syntax Tree (AST). We knew that the token public class Authentication was a High Value token. We knew that the token // TODO: fix this later was a Low Value token.
If you treat them equally, your search results will be garbage. We need Structure-Aware Indexing. Before we embed a document, we must pass it through a “Structure Parser” that assigns weights based on hierarchy.
- Tier 1 (High Weight): Titles, Headers, Class Definitions, API Contracts.
- Tier 2 (Medium Weight): Body text, Function logic.
- Tier 3 (Low Weight): Footnotes, Comments, Disclaimers.
When the retrieval engine runs, it should boost the Class Definition chunks over the Comment chunks, even if the semantic similarity is the same.
2. The “Metadata” Injection (Contextual Enrichment)
The Naive Approach:
“Search for the chunk that matches the query.”
The Engineering Reality:
A chunk of text in isolation is often meaningless.
The “Flat Chunk” has lost its parents. It has amnesia.
The Solution:
We must Enrich the Index. When we index that chunk, we don’t just store the text. We inject the Parent Metadata into the vector itself.
- Original Chunk: “It increased by 5%.”
- Enriched Chunk: “[Document: Q3 Earnings] [Chapter: Revenue] [Section: North America] It increased by 5%.”
Now, the vector carries the weight of its context. When the AI retrieves it, it knows exactly what increased, without needing to read the whole document.
3. The “Invisible Tuner” (The Startup Opportunity)
The Naive Approach:
“Let the user tune the ‘Alpha’ value for Hybrid Search (Keyword vs. Vector).”
The Engineering Reality:
Developers don’t know what ‘Alpha’ means. They don’t know if they should use BM25 or Cosine Similarity. The complexity of “How much do I boost the Title?” should be hidden.
The Startup Opportunity:
There is a massive need for “ Context-as-a-Service.”
- Ingests raw data (PDF, Code, HTML).
- Auto-Detects the structure (e.g., “This looks like a Legal Contract”).
- Auto-Tunes the weights (e.g., “Boost the ‘Definitions’ section by 2x”).
- Serves the perfect context via API.
The user shouldn’t be turning knobs. The Service should be analyzing the corpus and tuning itself.
Conclusion: Respect the Hierarchy
Information has shape. A title is more important than a footnote. A class definition is more important than a comment. If you flatten your data into a generic vector soup, you destroy that shape. Context Engineering is the art of preserving the Hierarchy of Information so the AI can understand not just what was said, but where it fits.
Originally published at https://www.linkedin.com.
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