ML Case-study Interview Question: LLM-Driven RAG for Enterprise Q&A and Automated Technical Documentation.
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Case-Study question
A large technology company wants to build a centralized system that retrieves internal documentation and delivers concise answers to users’ questions about their tools, data, and services. They have thousands of tables and technical materials spread across multiple sources with inconsistent and often incomplete descriptions. They need an approach that allows employees to query these documents using natural language. They also want to automatically enhance the documentation for each table by generating detailed table descriptions with minimal manual intervention. Finally, they plan to extend the system to interpret raw text and extract specific entities like dates or data fields. Propose a detailed solution that uses Large Language Models (LLMs) to create a Retrieval-Augmented Generation (RAG) pipeline and an automated documentation mechanism. Explain how you would handle potential hallucinations from the model, improve coverage of critical topics, and reduce user confusion. Describe any relevant architecture, data pipelines, prompt design strategy, and production considerations.
Detailed Solution
A robust system involves designing a pipeline that stores the company’s technical documentation and queries it when a user asks a question. A central index of documents feeds the LLM with context. This process is known as Retrieval-Augmented Generation (RAG). The first step is indexing data from sources such as wikis and table descriptions. The second step is retrieval and final answer generation. The indexing component involves splitting documents into manageable chunks to optimize text retrieval. Tools like vector databases or similarity-based indexing can help in identifying relevant chunks when a question arrives.
Users query the system via a Question-Answering interface. An internal process retrieves relevant documents or chunks. The LLM is then tasked with blending the user’s question with the retrieved context to produce a final answer. The model is restricted to that context, mitigating hallucinations.
Incomplete documentation is improved by employing an automated generation approach. The system prompts the model with any existing details about a table, such as field definitions or usage. The system instructs the LLM to produce a structured summary with a consistent format. When some information is missing, the generated content might be insufficient, so the team must revise the prompt or augment the source data. The final output is shared with table owners for feedback.
Another enhancement uses function-calling features in modern LLMs. Natural language inputs like “I want a booking with an expert on dashboards next Monday” map to structured data that the booking system can process. The model extracts the user’s intended topic (dashboards) and interprets “next Monday” as a precise date. The call to a “schedule function” or equivalent interface ensures that the final output is a structured field, such as {"topic": "dashboards", "date": "2025-04-07"}, which the booking system can use.
Testing is crucial. The team must systematically feed queries related to vital company processes and confirm correctness. If the model invents answers, it indicates missing documentation or an incorrect retrieval step. This process keeps the model’s domain knowledge tightly scoped to what exists in the knowledge base.
Maintaining performance involves analyzing feedback, refining the retrieval mechanism, adjusting embeddings, and improving the underlying database of text. Costs are balanced by using smaller or cheaper models where possible, especially when the question is purely factual. Larger models are used only if advanced reasoning is required.
Under-the-hood Explanation
The RAG system centers on a vector store indexing mechanism. Each document chunk is embedded into a numeric vector. When a user’s query arrives, it is also embedded into a vector. The system computes a similarity metric to find the top matching chunks. Those chunks, along with the user’s question, are fed into the LLM. This approach reduces hallucinations by restricting the context to the relevant domain text.
When generating table documentation, the system runs a prompt that includes any known details about the table. The prompt instructs the LLM to create a standardized description. If certain details are absent, the system catches the potential shortfall and either alerts the user for further input or prompts the LLM to indicate that information is unavailable.
Function calling enforces structured outputs. An example code snippet with Python and an LLM interface might be:
import openai
def create_documentation(table_info):
response = openai.ChatCompletion.create(
model="your-llm-model",
messages=[
{"role": "system", "content": "You are a data documentation expert."},
{"role": "user", "content": f"Generate documentation for the following table: {table_info}"}
],
functions=[
{
"name": "createDocumentation",
"description": "Generates standardized documentation fields",
"parameters": {
"type": "object",
"properties": {
"tableSummary": {"type": "string"},
"fieldDetails": {"type": "string"}
},
"required": ["tableSummary", "fieldDetails"]
}
}
]
)
return response
The call to createDocumentation ensures the model’s output is parsed as a structured response. This approach fosters consistent documentation fields.
How to handle complex queries with incomplete data?
A user might ask, “How do I set up a data flow for weekly reporting?” If the documentation lacks details, the system can only answer partially. One step is to prompt the LLM to state the missing topics. This alerts engineers to add relevant coverage to the documentation. Another solution is to supplement the knowledge base with living documents that teams maintain. Each incomplete query reveals gaps that must be filled.
What if the LLM still fabricates data?
Strict prompt guidelines reduce hallucinations. One additional safeguard is to store multiple candidate answers and compare them or incorporate a chain-of-thought approach internally. Another option is to build a final layer that checks references. If the system cannot match an answer with a known reference, it replies with an “Insufficient data” message. This stops the model from inventing details.
How do you maintain version control and updates for documentation?
It is best to attach version identifiers to each chunk of text. When data owners modify a table’s structure or add new fields, the indexing pipeline reprocesses that documentation and updates the stored embeddings. This ensures that recent changes are quickly reflected in the answers. Dev teams can monitor logs to verify that updates are triggered properly.
How do you ensure performance and scalability?
Vector searches grow heavier as the index expands. Horizontal scaling of the vector database helps. Sharding or distributed retrieval can spread queries. The model’s API usage can also be cached, especially for common queries. On the client side, asynchronous call patterns reduce latency. If real-time updates are not essential, batch re-indexing can be scheduled to manage resource usage.
Why not use simpler models?
Some tasks can be handled by keyword search or rule-based extraction. If the question is straightforward and does not need advanced language reasoning, an expensive LLM is not required. Hybrid setups blend classical search with LLM-based solutions. This combination can lower costs while retaining the benefits of natural language understanding. The best approach is measured experimentation with fallback strategies for easy tasks.
How do you evaluate success?
Teams track user satisfaction via feedback forms or ratings after each query. They also analyze metrics like the accuracy of extracted entities, time saved compared to manual searching, and the reduction in support tickets. If key metrics fall short, it signals that the retrieval pipeline or the data coverage needs refinement.
What if the system receives sensitive requests?
Prevent unauthorized answers by assigning labels or security classes to documents. At retrieval time, the system checks user permissions. Documents outside the user’s access scope are excluded from the final context. Auditing logs of queries and answers helps. If the question demands private information, the system refuses or sanitizes the response.
How do you handle domain-specific acronyms and custom vocabulary?
Part of the pipeline includes training or fine-tuning the embeddings with domain context. It recognizes internal acronyms so that the vector search becomes more accurate. Users might type “Where can I find ACME config?” The model embedding process learns that “ACME” is an internal reference and associates it with the correct documents. If acronyms change, the pipeline is updated with new synonyms or expansions.
How does function calling help produce structured data?
It replaces unconstrained text responses. When the user says “Book me a data architecture consultation next Wednesday,” the prompt instructs the model to fill a JSON with fields like {"service": "data architecture", "date": "2025-04-02"}. This eliminates guesswork in parsing the LLM’s text. Downstream systems then directly act on the structured result.
How to adapt this solution to multimodal inputs?
If the company wants image or voice data integrated, a multimodal approach can help. They can feed the user’s text or voice transcript to the same retrieval pipeline. For images containing text or diagrams, an OCR step is added before retrieval. This approach broadens the system’s utility but requires additional modules for image or speech processing.
Why is automated documentation generation important?
Maintaining thousands of tables manually is slow and error-prone. The LLM speeds up drafting. Table owners then review for correctness. This short feedback loop yields consistent and thorough coverage. It ensures that future queries about these tables will retrieve relevant information. Over time, the system’s utility grows as more tables have robust documentation.
How would you finalize production deployment?
Everything goes behind a scalable API. Authentication ensures only authorized employees can query or update. Monitoring checks query volume, response latency, and user feedback. Regular updates add new docs, reindex changes, and refine prompts if feedback indicates confusion. The final step is marketing it internally so employees adopt it, reducing the overhead of searching or guessing documentation paths.
How to handle future expansions?
New use cases might demand referencing chat logs or support tickets. This requires an updated index. The system’s design is flexible enough to add new data sources. Each source is chunked and embedded, the retrieval pipeline is extended, and the LLM’s context is updated. If the model struggles, a specialized fine-tuned variant can be used. The design remains modular, so each part can scale or adapt independently.