Agentic RAG Strategies for Consumer-Facing Chatbot
Lessons from Building consumer-facing Agentic RAG Application
When you start building a RAG (Retrieval-Augmented Generation) application, it’s easy to get sucked into the hype of "Agentic Workflows" or massive architectural shifts. But after spending quite a bit of time playing around with different strategies on a consumer-facing Agentic RAG app, I’ve realized something: you don’t always need a massive overhaul to see a huge jump in performance.
In fact, some of the biggest wins I’ve had lately came from small, tactical changes that were relatively low effort to implement but improved the quality specially around accuracy and latency.
If you're building a consumer-facing app where every second of latency and every bit of "hallucination" matters, here are the strategies that actually worked for me.
Stop Searching with Raw User Queries
If a user asks a simple, conversational question like, "is it available in blue color?", and you throw that exact string at your vector database, you’re going to get back a lot of garbage.
The problem is that a short user query rarely has enough "semantic meat" to match the technical or descriptive language in your knowledge base.
The Fix: Multi-Angle Query Reformulation Instead of one search, I use a lightweight LLM (think Claude 3 Haiku or a similar fast model) to act as a pre-processor. It takes the initial query—and more importantly, the previous conversation turns—and reformulates it into 2 or 3 different search queries.
By hitting the knowledge base from different angles concurrently, you solve the "it" problem (where the user refers to an object in the previous message) and significantly increase the chance of finding the right document. Since these calls happen in parallel, the latency hit is negligible compared to the massive jump in retrieval quality.
HyDE: The "Fake It 'Til You Find It" Strategy
If query reformulation is about rephrasing, HyDE (Hypothetical Document Embeddings) is about hallucinating a bridge.
The idea is to ask your LLM to write a "hypothetical" answer to the user's question before you even touch your database. You then take that fake answer and use its embedding to search your knowledge base.
It sounds counterintuitive—why search with a hallucination?—but it works because vector databases are better at matching "Answer-to-Answer" than "Question-to-Answer." By creating a hypothetical document that mirrors the structure of what you're looking for, you bridge the semantic gap that often trips up standard RAG.
Knowledge Base Relevancy Scores
There’s a common temptation to set a hard threshold on your knowledge base documents (e.g., "only return documents with a score > 0.8"). In my experience, this is a trap.
I’ve seen plenty of cases where a KB relevancy score is relatively low—think 0.48—but the document is actually the most vital piece for the final response. If your chunking strategy is bad, don't be so quick to kill those lower-scored results.
Thresholds can work as a safety net, but you need to be 100% sure your chunking is solid first. If you're missing the mark, look at your chunking strategy before you start tightening the score filters.
Reranking
If you feel like your chunking strategy is a bit messy or the retrieved documents are coming back a bit noisy, Reranking is going to save your context and make your RAG look better.
Standard vector search is great at finding the top 50 "potentially" relevant documents, but it’s not always great at picking the #1 most relevant one. By adding a reranking step—using something like a Cohere rerank model—you can take those top 50 results and let a specialized model sort them based on the actual query.
Reranking could make the answer significantly more relevant and lead to a much better LLM response. It’s a relatively quick implementation that essentially cleans up the mess left behind by mediocre chunking or basic vector similarity.
The Struggle with Amazon Bedrock Guardrails
I have to be honest here: Amazon Bedrock Guardrails haven't appeared very "intelligent" to me so far. They feel more like overly cautious ousins than smart filters.
Because they aren't truly LLM-based and rely on a heuristic approach (regex, keyword filters, and word-matching), they lack the nuance needed for complex user intent.
- Latency: They have an effect on your latency, because the input has to go through Guardrail validation before it even reaches the LLM.
- False Positives: We’ve seen a lot of legit user queries get blocked because they triggered a "keyword" that was perfectly innocent in context.
I’m still looking for a smarter solution for this.
Easy Wins: Caching and Speed
If you aren't using System Prompt Caching, you're leaving money and performance on the table.
Whether you're using the Bedrock Converse API or the Claude SDK, spend some time looking at your parameters. Getting those CacheReadInputToken hits feels great—you’re reducing latency for the user and saving the business money on every single call. At the very least, cache your system prompt; it's the most static and repetitive part of your token usage.
Use Faster and relatively lighter-model
Also, don't over-engineer your model selection. For a normal Agentic RAG agent, you don’t need a "thinking" or "reasoning" level model. Use a faster model. In a consumer app, 500ms saved in latency is usually worth more than a tiny bump in "intelligence" that the user might not even notice.
Prompt Engineering
Prompt Engineering is still my weakest point. Honestly, these models just don’t listen to me sometimes. I say one thing, they do another—it’s like we have a bad relationship and poor communication right now. 😅
I’ve tried everything, but we’re still not quite on the same page. Don't forget to say "please" to your LLM in the system prompt. It didn't work for me, but it might work for you if the LLM actually likes you.
I’ll be diving into the "Agentic" side of RAG in my next post, but for now, these were the small-effort, high-reward wins I wanted to share.