Why “Just Ask the Bot” Is Harder Than It Looks: A Friendly Walk-Through of Enterprise-Grade RAG

by Vikram Srinivasan

Precision, Recall, and the Great Tug-of-War

Think of a grocery run:

Precision Recall Definitions

Grab every vaguely round, reddish thing and recall soars—but oranges creep in, tanking precision. Play it safe with only the shiniest Fuji apples, and recall nosedives.

That curved line in Figure 1 is the efficiency frontier. It shows the best trade-off you can get before boosting one metric inevitably hurts the other. The orange dot up in the corner—100 % precision and 100 % recall—is where every business user secretly wants to live.
‍
‍

Why Web Search Feels Forgiving

On Google you rarely notice that gap. There are billions of pages; if the first screen answers your question, you move on. You don’t feel the missing five percent because you never knew it existed. And if you need more, you scroll, tweak the query, or click to page two. Precision and recall are quietly your problem to balance, and most of us have trained ourselves to do it subconsciously.

‍

Enterprise Search: “Known Knowns” vs. “Unknown Unknowns”

Former U.S. Defense Secretary Don Rumsfeld famously sorted knowledge into boxes:
‍

“There are known knowns—things we know that we know.
There are known unknowns—things we know we don’t know.
And there are also unknown unknowns—things we don’t know we don’t know.”

‍

In enterprise search, the customer is dealing with a known known. They remember writing that risk-model appendix last November; it exists somewhere in the corpus.

For the RAG system, however, that very document can be an unknown unknown. If it sits in a quirky format, a forgotten SharePoint, or below the top-N retrieval cutoff, the system literally “doesn’t know that it doesn’t know.” The gap between those viewpoints is what makes 100 % precision 100 % recall so hard.

1. Classic RAG—Fast but Fragile

Retrieve the top N snippets ➜ Generate a friendly summary. Elegant!—until clause #17 never meets the generator and the answer is polished yet incomplete.

‍

2. Question Decomposition—Hunting the Unknowns

Large language models now break your chunky query into sub-questions:

“How did Q1 revenue compare to last year and what drove the difference?”
‍
⟶ “What was Q1 revenue this year?”
⟶ “What was Q1 revenue last year?”
⟶ “List drivers cited in the finance memo.”
‍

Each sub-query launches its own search, expanding recall without flooding precision. Why it helps: instead of one fishing net, you throw three. Why it’s not a silver bullet: miss on any sub-query and the stitched answer still skips a piece of the puzzle.

3. Agentic RAG—A Sleepless Research Intern

Think of an agentic system as an intern who never tires and willingly admits, “I’m not sure yet”:

1. Draft an answer.
2. Judge: Is this accurate and complete?
3. If not, reformulate the query, fetch fresh documents, and loop.
   ‍

Because the agent keeps digging until it’s satisfied, it nudges the curve closer to that elusive (1, 1) dot—at the cost of extra compute time and architectural complexity.

Note: Decomposition and agentic RAG are one family of tricks to surface unknown unknowns. Hybrid keyword ✚ vector retrieval, ontology-driven ranking, exhaustive background indexing, and other approaches can—and usually should—join the toolbox.

‍

The Elephant in the Stack: Messy Enterprise Data

Chasing precision/recall perfection is tough enough; real-world corpora throw extra curveballs:
‍

Enterprise RAG Challenges

‍

Taming these quirks calls for relevance tuning (boost contract libraries, down-weight Slack banter), fine-tuning (retrain the language model on internal prose), and rock-solid metadata hygiene. In other words, the “plug-and-play” phase really starts after you plug it in.

‍

Takeaways

1. Physics, not laziness: Precision vs recall is a law of trade-offs

2.Known knowns vs unknown unknowns: Users expect certainty; the system starts blind.

3.Chatbots delete the scroll-buffer: One-shot answers magnify retrieval misses.

4.Decomposition & agentic loops chase the unknowns: They push the curve but can’t rewrite it.

5.Messy data is the final boss: Formats, acronyms, entitlements, versioning all need bespoke work.

6.Customization is where the magic happens: Expect relevance weighting, ontologies, and security wiring before users cheer.

Bend the curve far enough and the gap shrinks until people stop noticing—but the curve never fully disappears. That’s the art (and the excitement) of building reliable RAG for the enterprise.

‍

For more insights from Vikram on enterprise AI, market intelligence, and what we’re building at Needl.ai, subscribe to his substack.

‍

‍