ML Case-study Interview Question: RAG Pipeline for Context-Sensitive AI Assistant in Software Platforms
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Case-Study question
You are tasked with designing a context-sensitive AI assistant for a complex software platform that integrates multiple third-party services, vector databases, and data pipelines. The AI assistant must appear directly in the user interface, guide users with accurate, up-to-date answers, and reduce reliance on external documentation. How would you build this system end-to-end, ensure high-quality retrieval, prevent hallucinations, and measure performance over time?
Proposed Solution
Use a Retrieval Augmented Generation (RAG) pipeline where documentation and support conversations are continuously ingested into a vector database. Convert incoming text into embedding vectors, store them, and then retrieve them at query time using similarity search. Integrate the assistant within the main product interface and pass contextual hints to the retrieval step.
Include multiple data sources (documentation pages, user Q&A records, etc.) so the pipeline automatically improves when new or corrected content is added or old content is removed. When a user asks a question, feed that question (plus relevant context) into the retrieval step, rank or rerank the results, and supply the most relevant matches to the Large Language Model. Strictly instruct the model to only use retrieved text and produce a fallback answer if none of the retrieved text is relevant.
Below is a typical similarity metric used to measure how close the user query and knowledge base text embeddings are:
Here:
u and v are the embedding vectors for the user query and a candidate text.
u dot v denotes the dot product between u and v.
||u|| and ||v|| denote the Euclidean (L2) norms of u and v.
Filter out results that score below a chosen relevance threshold. Provide these filtered results to the model as context. In your prompt, instruct the model never to fabricate details outside the retrieved content. Once the model returns its answer, store user feedback (thumbs-up or thumbs-down) for monitoring and iterative improvements.
Building the pipeline
Set up ingestion scripts (or connectors) to pull text from documentation, chat transcripts, and relevant knowledge bases. Send each text block to an embedding model to generate vectors, then store these vectors in a vector database. For retrieval, embed the user query with the same embedding model, perform a vector similarity search, rerank the top matches, and discard irrelevant ones.
Query Rewriting or Contextual Topics
If a user’s question is ambiguous, incorporate the page or feature context to refine the query. For instance, if the user is on an “API Keys” page and types “How does it expire?”, prepend or append the topic “API Keys” to the query to boost retrieval quality.
Anti-Hallucination Prompting
Instruct the model to base its answer strictly on the provided retrieved text. If none of the retrieved text matches, it should declare it cannot find an answer. Warn it not to reveal user names or other private details found in the source text. This lowers hallucinations.
Monitoring
Add a user feedback mechanism to gather ratings for each response. Track queries that yield poor relevance scores or large numbers of negative feedback. This can point to missing data or unclear documentation. Periodically review logs to refine thresholds or add additional training data for the reranking model.
Follow-Up Question 1
How do you handle a scenario where the user’s question yields minimal or no relevant results from the vector database, yet you suspect the knowledge might exist in less-structured data?
Answer and Explanation Push any additional or less-structured data sources into the pipeline. If the pipeline originally focused on official documentation, expand it to user forum posts, Q&A threads, or other relevant text. Break large documents into smaller chunks. Embed them individually to avoid missing partial matches. If the pipeline still finds zero relevant matches, your prompt instructs the model to admit no answer is found. Over time, refine chunking, improve text cleaning, or include a fallback approach (like direct LLM reasoning with disclaimers).
Follow-Up Question 2
Why is it beneficial to use a reranking model instead of only relying on high similarity scores from the vector database?
Answer and Explanation A reranking model examines each (question, document) pair holistically rather than just embedding proximity. The vector database can produce items that are partially relevant. Reranking calculates an explicit relevance score that can reveal if a top vector match is actually off-topic. Lower-threshold results are discarded to reduce confusion for the model.
Follow-Up Question 3
How would you integrate a real-time user feedback loop to refine this system?
Answer and Explanation Tag thumbs-up or thumbs-down feedback on each answer. Store that rating with the question, the final text used for retrieval, and the final LLM output. Periodically analyze negative ratings to see if retrieval or prompting is failing. Use insights to refine context usage, adjust the retrieval threshold, or correct data sources. This feedback pipeline automatically surfaces holes in your documentation or missed updates in the knowledge base.
Follow-Up Question 4
What steps ensure that newly published documentation updates are reflected immediately in the assistant’s responses?
Answer and Explanation Schedule frequent ingestion runs or use a real-time webhook approach. Whenever new documentation is pushed or revised, re-embed the changed text and update the vector database. Remove outdated vectors if text was deleted. Confirm the pipeline that reads from the database has no caching or stale indexing. This keeps the LLM context fresh and in sync with the newest data.
Follow-Up Question 5
How would you optimize costs if your vector database grows significantly?
Answer and Explanation Limit the stored chunks to only the most useful. Archive older or less-accessed content. Use a smaller or more efficient embedding model if your domain is narrower. Adjust the similarity threshold so fewer documents are passed to the reranking model. Use a smaller LLM if possible. Implement usage-based retrieval so queries tap into relevant indexes first, skipping large indexes that seldom produce needed answers.
Follow-Up Question 6
What strategies would you use to reduce hallucinations beyond filtering and prompt instructions?
Answer and Explanation Use strict system-level prompts that override user instructions. Provide minimal text from retrieval so the model sees only relevant details. Shorten the conversation history to reduce extraneous context. Employ chain-of-thought prompting sparingly, ensuring the model remains grounded. Provide explicit disclaimers in the user interface, for example telling users the assistant is referencing only known data. Periodically audit transcripts for hallucinations and refine your pipeline or add new data.
Follow-Up Question 7
How do you ensure the assistant adapts to newly introduced features that are not yet documented?
Answer and Explanation Encourage team members to add placeholder docs or short notes in a shared repository. Include them in the ingestion pipeline. If official documentation is delayed, add product manager or developer Q&A data. If no text exists, the assistant simply returns that no answer is found. Track repeated queries about missing features and flag them so documentation teams know to add or update content.
Follow-Up Question 8
How would you approach advanced analytics or dashboards to understand assistant usage and performance?
Answer and Explanation Add logging for each query, retrieval results, final answer, and user feedback. Build metrics to visualize average relevance scores, average user rating, and frequently queried topics. Graph usage volume over time to see adoption. Segment by user actions (e.g., users who consulted the assistant vs. those who did not). Use these insights to pinpoint high-traffic areas in need of more thorough docs.
Follow-Up Question 9
What if your client’s data sources contain personally identifiable information?
Answer and Explanation Preprocess data to detect and mask personal data. Maintain a strict policy to anonymize or remove user names, emails, or phone numbers. Configure your pipeline to discard data chunks that do not meet compliance standards. In prompts, instruct the model to never expose private information. Implement strong access controls so only authorized systems can query the vector index containing sensitive embeddings.