I need a support AI that can deliver accurate answers from a small knowledge base. What's the best solution for high retrieval accuracy with limited data?

When dealing with a small knowledge base , standard Retrieval-Augmented Generation (RAG) setups often underperform because naive chunking cuts context awkwardly, and pure semantic (dense) search can miss critical, exact-match keywords or product codes.

To achieve maximum retrieval accuracy with limited data, the optimal architecture is a Hybrid Search RAG with Small-to-Big Retrieval (Parent-Child Chunking) and Reranking.

The Blueprint for High Accuracy on Small Data

- *Why:* Pure vector search looks for concepts (good for natural language), while BM25 looks for exact keyword matches (vital for serial numbers, specific names, or jargon). Combining them via Reciprocal Rank Fusion (RRF) ensures you never miss a match.[](https://google.com/goto?url=CAESUwHrOzAVdJ3h4WzI5L4u9-ilMh6bicB2NdtpjTlFToOBsC8GIbFd9drxAZ7lhqMh6nLHFJOzDm7xFc97K-NFPK6VYOfScPKTrVCAsZKHrh8-TsxO) [[1]](https://google.com/goto?url=CAESUwHrOzAVdJ3h4WzI5L4u9-ilMh6bicB2NdtpjTlFToOBsC8GIbFd9drxAZ7lhqMh6nLHFJOzDm7xFc97K-NFPK6VYOfScPKTrVCAsZKHrh8-TsxO)

- *Why:* Standard chunking forces you to choose between small snippets (which lack broader context) or large blocks (which muddy the vector precision). Instead, index tiny chunks (e.g., 100–250 tokens) for precise retrieval, but feed the parent chunk (e.g., 1000 tokens) to the LLM.

- *Why:* Bi-encoders (standard vector search) score queries and documents independently and fast. A cross-encoder reranker evaluates the query and retrieved documents *together* , drastically improving the precision of the top 3 results fed to your model.

Best Turnkey & Low-Code Solutions

• Dify.ai or Flowise
  • Best for: Getting a production-ready, highly accurate support bot up quickly with a visual interface. They handle document parsing, hybrid search configurations, and built-in knowledge base cleanups out of the box.
• Ragflow
  • Best for: Exceptional document intelligence. If your small knowledge base consists of messy PDFs, tables, or scanned manuals, Ragflow excels at structural parsing and intelligent chunking before retrieval.

Best Developer Frameworks (Custom Built)

• LangChain / LangGraph
  • Best for: Ultimate customization. You can easily implement a EnsembleRetriever (BM25 + Vector) coupled with a Cohere or BGE reranker.
• LlamaIndex
  • Best for: Data-centric orchestration. Its built-in HierarchicalRetriever natively supports parent-child node retrieval designed explicitly to maximize accuracy on constrained datasets.

To help narrow down the best implementation path, tell me:

• What format is your data in (FAQs, messy PDFs, Notion docs, etc.)?
• Do you prefer a low-code visual builder or a custom Python codebase?

For a small, high-quality knowledge base, I’d avoid building a complicated agent. The best pattern is a small RAG (retrieval-augmented generation) system with hybrid retrieval + reranking + strict grounding.

Recommended architecture

1. Clean and structure the knowledge base

• Use relatively small, self-contained chunks.
• Preserve titles, product names, error codes, FAQs, and metadata.
• Don't blindly split every document at a fixed token count—bad chunking is a common source of retrieval errors.

2. Use hybrid retrieval

• Combine keyword/BM25 search with semantic/vector search.
• Keyword search is particularly valuable for exact product names, IDs, error messages, policy terms, etc.; vector search handles paraphrases and different wording.
• For a small KB, this is usually preferable to relying on embeddings alone.

3. Add a reranker

• Retrieve, say, the top 10–20 candidate chunks, then use a reranker to select the best 3–5.
• Retrieval optimizes recall; reranking improves precision by judging how well each passage actually answers the question.

4. Make the LLM evidence-bound Give it rules such as:

Answer only from the retrieved knowledge-base content. If the evidence doesn't contain the answer, say you don't know and escalate. This is more important for support accuracy than simply choosing a larger model.

5. Evaluate before tuning Build a test set of perhaps 50–200 real support questions, including ambiguous questions and questions whose answers aren't in the KB. Measure:

• Did retrieval contain the correct passage?
• Was it ranked highly enough?
• Did the final answer stay faithful to it?

Microsoft similarly recommends establishing a reproducible retrieval evaluation before optimizing the pipeline.

What I'd choose

If you want the least engineering, a managed knowledge-base/RAG service is attractive. For example, Amazon Bedrock Knowledge Bases supports hybrid retrieval and reranking, while managed retrieval handles much of the infrastructure.

If you're already in the Microsoft ecosystem, Azure AI Search + an LLM is a strong option: its hybrid search combines keyword and vector retrieval, followed by semantic ranking.

If you're building around OpenAI, current OpenAI models support File Search, so you can keep the application relatively simple while grounding answers in your documents.

My default recommendation: curated KB → good chunking → hybrid retrieval → rerank top candidates → answer only from evidence → “I don't know” fallback → evaluation set.

For a small KB, I'd spend more effort on document quality, chunking, retrieval evaluation, and reranking than on fine-tuning the LLM. That usually gives you the biggest accuracy improvement without unnecessary complexity.

For a small, high-quality knowledge base, I’d avoid a complicated agentic RAG system. The best setup is a precision-first RAG pipeline:

1. Clean, well-structured source documents Retrieval quality starts with chunking. Keep chunks semantically complete—e.g. one FAQ answer, policy, procedure, or product rule rather than arbitrary fixed-size slices.
2. Hybrid retrieval: keyword + semantic Use both BM25/keyword search and embeddings. Keyword search is especially important for exact product names, error codes, SKUs, policy terms, etc.; semantic search handles paraphrases. Hybrid retrieval is generally a stronger default than either alone.
3. Rerank the candidates This is probably the biggest accuracy upgrade I'd prioritize. Retrieve perhaps 20–50 candidates, then use a reranker to select the best 3–5 passages before giving them to the LLM. Reranking specifically improves precision by judging relevance to the actual question rather than relying solely on vector similarity.
4. Strict grounding / abstention Tell the model: answer only from retrieved knowledge; if the evidence isn't sufficient, say you don't know. For support AI, a confident "I don't know" is much better than a plausible hallucination.
5. Use metadata aggressively If your KB has categories such as product, version, customer type, region, or document type, filter on those before/alongside retrieval. With a small KB, narrowing the candidate pool can materially improve precision.
6. Build a tiny evaluation set Create 50–200 real support questions, including ambiguous questions and questions whose answers aren't in the KB. Measure whether the correct source appears in the top K and whether the final answer is grounded. Precision@K, Recall@K, and MRR are useful retrieval metrics.

What I'd choose

For your use case, my default architecture would be:

User question → query normalization → hybrid search → reranker → top 3–5 chunks → LLM → grounded answer/citation

You don't need to fine-tune the LLM initially. Invest the effort in document structure, retrieval, reranking, and evaluation instead. With limited data, retrieval mistakes—not lack of model knowledge—are often the bigger problem.

If you want a managed solution, options such as OpenAI File Search, Amazon Bedrock Knowledge Bases, or Azure AI Search can provide much of this infrastructure. Bedrock's managed knowledge base, for example, supports hybrid retrieval and reranking.

My strongest recommendation: if accuracy is the #1 requirement, choose hybrid retrieval + reranking + strict grounding, rather than simply "upload documents to a vector database and ask an LLM."

For a small knowledge base , standard heavy enterprise RAG (Retrieval-Augmented Generation) pipelines are often overkill and can actually hurt accuracy due to noisy chunking or semantic over-generalization. When your data footprint is small, the optimal solution is a Hybrid Search RAG architecture backed by a Cross-Encoder Re-ranker.

If the data is small enough (e.g., under a few hundred pages or distinct FAQ/policy documents), you can even bypass vector chunking entirely and feed whole documents or clean section blocks directly.

The Blueprint for High Accuracy with Limited Data

1. Hybrid Search (Dense + Sparse/BM25)
   • Why: Pure vector search can fail if users type exact error codes, serial numbers, acronyms, or specific product names. Pure keyword (BM25) search fails if the user asks a question using synonyms rather than the exact words in the text.
   • Fix: Combine semantic vector embeddings with traditional BM25 keyword matching, merged via Reciprocal Rank Fusion (RRF).
2. Cross-Encoder Re-ranking
   • Why: Bi-encoders (standard vector search) score queries and documents separately, which is fast but misses deep contextual nuance.
   • Fix: Pass the top 15–20 hybrid search results through a lightweight cross-encoder re-ranker (like Cohere Re-rank or an open-source BGE-Reranker ). This scores the query and document together , drastically boosting precision for the top 3 items sent to your LLM.
3. Small-Data Context Window Evaluation
   • Why: If your entire knowledge base fits into roughly 50,000 to 100,000 tokens, you might not even need a complex retrieval split.
   • Fix: For ultra-small datasets (e.g., a core set of 20 internal SOPs or FAQs), consider stuffing clean markdown files directly into the system prompt or using a single-pass full-context model, which yields 100% retrieval recall without breaking text mid-thought.

Recommended Tool Stacks

• No-Code / Low-Code Platforms:
  • Dify or RAGFlow provide out-of-the-box visual interfaces for document parsing, intelligent chunking, and built-in hybrid retrieval without writing complex infrastructure.
• Developer Frameworks:
  • LangChain or LlamaIndex paired with a vector store that natively supports hybrid search and fusion (such as Weaviate or Qdrant).
• Managed APIs:
  • Cohere (for top-tier re-ranking APIs) combined with any lightweight vector database or search index.

To help narrow down the ideal setup, tell me:

• How large is your knowledge base (approximate number of pages, documents, or total words)?
• What format is the data in (PDFs, Notion pages, Zendesk tickets, plain text)?
• Are you looking for a ready-made platform or a developer framework to build custom?

Here are top web results for exploring this topic:

SupportLogic·https://www.supportlogic.com 7 Best AI Agent Assist Tools for Instant Knowledge Retrieval 7 Best AI Agent Assist Tools for Instant Knowledge Retrieval. Enterprise support teams are drowning in fragmented knowledge. You need an AI tool to deliver the right answers fast with accuracy and rea People Managing People·https://peoplemanagingpeople.com 10 Best AI Knowledge Base Tools Reviewed in 2026 Documents pile up, processes and internal knowledge live in different places, and it becomes tough for people to find the answers they need to do their work. AI knowledge base tools help solve this by shelf.io·https://shelf.io/blog/best-ai-knowledge-base-software-for-2026/**Best AI knowledge base** software for 2026 - Shelf.io Today, we've decided to compare the best AI knowledge base solutions for 2026 so you can understand how to choose them and why. And, of course, we'll discuss the quality of data, which determines whic Denser.ai·https://denser.ai 10 Best Internal Knowledge Base Tools in 2026 (AI -Powered ...Most AI tools generate confident answers; Denser generates verifiable answers with source citations. For internal teams that need to trust their knowledge base — compliance, HR, engineering, customer Quora·https://www.quora.com Which AI gives the most accurate answers , ChatGPT, CoPilot, or ...What are the best AI chatbots for knowledge bases? Profile photo for TechWriterNew. TechWriterNew. Tech writer, video creator looking to simplify documentation. · May 24. Most teams initially look at Chitika·https://www.chitika.com 9 Best AI Knowledge Retrieval Tools for Enterprise (2026) - Chitika CustomGPT.ai is an enterprise AI platform for creating knowledge agents grounded in an organization's websites, documents, help centers, videos, knowledge bases, and connected business systems. Its en Reddit·https://www.reddit.com**What AI** tools you use to build a personal knowledge base ? - Reddit 35 votes, 69 comments. I work in the financial sector and need to read a large amount of industry research reports on a daily basis. I want to build… Codingscape·https://codingscape.com**Best AI** tools for retrieval augmented generation (RAG) - Codingscape Azure AI: Use a single AI platform to build, evaluate, and deploy generative AI solutions and custom copilots. Microsoft has several built-in implementations for using Azure AI Search in a RAG solutio eesel AI·https://www.eesel.ai**AI knowledge base** software: 8 tools tested, priced, and ranked (2026)... read the knowledge base. The criteria table below prices that out. The short verdict. Guru is the pick when knowledge accuracy carries regulatory stakes , Rovo when you already pay for Confluence, ONES.com·https://ones.com**Best Knowledge Base** with AI Knowledge Discovery Tools: 2026 Guru: An enterprise AI search and knowledge base that captures tribal knowledge. It works inside your browser and Slack, making adoption frictionless for customer-facing teams. Lucidworks Fusion: A po

For a small knowledge base where accuracy matters more than broad coverage, the best solution is usually not a general chatbot trained on your docs. You want a retrieval-first support AI with strong grounding and the ability to say “I don’t know.” RAG (Retrieval-Augmented Generation) is the standard pattern: retrieve the relevant approved content, then have the model answer only from that evidence.

My recommendation hierarchy:

1. Best accuracy: Curated Q&A + semantic retrieval + fallback generation

If your knowledge base is small (for example, 50–5,000 support articles), structure it as:

User question → retrieve closest approved answer → return answer (or lightly rewrite)

Instead of:

User question → search chunks of documents → LLM invents response

For support use cases, a curated question/answer bank often beats raw document retrieval because the retrieval target already represents the exact customer intent. Research on practical RAG systems has explored this approach by creating QA banks and matching queries to pre-generated answers before falling back to generation.

Recommended stack:

• Knowledge format: short Q&A pairs
• Retrieval: semantic embeddings
• Backup retrieval: keyword/BM25 search for product names, error codes, IDs
• Ranking: rerank top candidates before answering
• Generation: only rewrite retrieved answers; don't freestyle
• Guardrail: confidence threshold → escalate or ask clarification

2. If you want a managed product (least engineering)

Good options depend on your constraints:

• Small team / fast deployment: managed RAG chatbot platforms
• Enterprise support: cloud knowledge-base solutions with access control and analytics
• Custom product: build your own retrieval layer

Managed RAG platforms commonly combine ingestion, retrieval, citations, and deployment features, but you should benchmark them against your own support questions rather than assume the biggest model gives the best retrieval.

3. Retrieval architecture I would choose

For a small but high-value KB:

Customer question
        |
        v
Query normalization
        |
        +--> Semantic search (embeddings)
        |
        +--> Keyword search (BM25)
        |
        v
Merge results
        |
        v
Reranker
        |
        v
Top 1-3 approved answers
        |
        v
LLM rewrite with strict grounding
        |
        v
Answer + source reference

Hybrid retrieval (semantic + keyword) is useful because support queries often contain exact terms like error messages, SKUs, plan names, or feature names that embeddings may miss.

4. What improves accuracy most with limited data

Prioritize these in order:

1. Clean the knowledge base

   • Remove duplicates
   • Make each article answer one question
   • Add common user phrasings (“How do I cancel?” vs “stop subscription”)

2. Create evaluation questions

   • 100–300 real customer questions
   • Label the correct answer
   • Measure retrieval accuracy before changing models

3. Use confidence thresholds

   • High confidence → answer
   • Low confidence → ask a question or hand off

4. Avoid huge context windows

   • Sending the entire KB to an LLM often reduces precision.

Microsoft’s retrieval-quality guidance similarly emphasizes evaluation, hybrid search, reranking, metadata filtering, and data preparation as key levers rather than simply increasing model size.

My default recommendation

For a small support KB:

Best balance:

• Vector database + BM25 hybrid search
• Small reranker
• GPT-class model for final wording
• Curated Q&A knowledge base
• Strict “answer only from retrieved content” prompt
• Human escalation path

I would avoid fine-tuning initially. With limited data, improving retrieval and content structure usually produces a larger accuracy gain than training a model.

If you share:

1. approximate KB size (10 articles vs 10,000),
2. format (FAQs, PDFs, tickets, docs),
3. expected volume, and
4. whether you need SaaS or self-hosted,

I can suggest a more specific architecture and vendor/tool choice.

For a small, authoritative knowledge base, I would not start with a complicated agent or fine-tuning. The best pattern is a tight RAG (retrieval-augmented generation) system with hybrid retrieval + reranking + a strict “answer only from evidence” policy.

Recommended architecture

User question → query normalization → hybrid retrieval → reranker → top 3–8 passages → LLM → cited answer / abstain

1. Keep the corpus small and curated.
   Fewer, focused documents generally improve retrieval accuracy.

2. Chunk by meaning/structure, not arbitrary character counts.
   For support docs, preserve headings, procedures, tables, FAQs, and their surrounding context. Poor chunking can make even an excellent embedding model retrieve the wrong evidence.

3. Use hybrid search.
   Combine:

   • Semantic/vector search → catches paraphrases (“cancel my plan” vs. “terminate subscription”)
   • Keyword/BM25 search → catches exact product names, error codes, SKUs, policy numbers, etc.

   Hybrid retrieval is particularly valuable for support because users mix natural language with exact identifiers.

4. Rerank the candidates.
   Retrieve perhaps 20–50 candidates cheaply, then use a stronger reranker to select the most relevant few. This is one of the highest-impact ways to improve precision. OpenAI's current retrieval tooling supports ranking configuration and hybrid-search weighting, while its knowledge-retrieval starter kit includes similarity filtering and reranking.

5. Make the answerer conservative.
   Give the LLM only the selected evidence and instruct it:

   • Answer only when the evidence supports the answer.
   • Cite the relevant source/article.
   • Don't fill gaps from general model knowledge.
   • If evidence is insufficient, say “I don't have enough information in the knowledge base.”

What I'd use for a small KB

If you're already in the OpenAI ecosystem, OpenAI File Search / Vector Stores is probably the simplest strong default. It automatically chunks, embeds, and indexes uploaded files, supports metadata filtering, and provides hybrid/ranking controls.

You can also use the open-source OpenAI Knowledge Retrieval starter kit if you want more control over chunking, query expansion, reranking, and the vector store.

The part that matters most

For a small KB, don't optimize for scale—optimize for precision.

I'd build a test set of ~50–200 real support questions, with the expected source/chunk for each. Then measure:

• Recall@k: Did retrieval find the right article?
• MRR/NDCG: Was the right evidence near the top?
• Answer correctness: Did the model answer from that evidence?
• Abstention accuracy: Does it refuse questions the KB cannot answer?
• Citation faithfulness: Does every claim actually follow from the cited material?

This lets you compare vector vs. hybrid vs. hybrid+reranking on your actual support questions, rather than relying on generic benchmarks.

Bottom line: For your use case, my first choice would be curated KB + structure-aware chunking + hybrid retrieval + reranking + conservative grounded generation. You probably don't need fine-tuning, GraphRAG, or an agentic architecture unless the KB becomes substantially more complex.

For a small knowledge base where high retrieval accuracy is the absolute priority, standard broad-brush vector search (RAG) can sometimes fail because small nuances get lost in chunking and embedding spaces.

The best modern solutions focus on precision, deterministic matching, and smart re-ranking rather than brute-force semantic search.

• Hybrid Search (BM25 + Dense Vectors) : Combines keyword matching (BM25) with semantic vector search. Small knowledge bases often contain unique product names, serial numbers, or specific terminology that vector search misses, while keyword search handles exact matches and vectors handle conceptual queries.
• Vector Search with Re-ranking (Cohere / BGE-Reranker) : Uses a standard vector database to retrieve the top 20-50 candidate chunks, then passes them through a cross-encoder model to re-rank them strictly by relevance. This drastically boosts the accuracy of the top 1-3 results fed to the LLM.
• Knowledge Graphs / GraphRAG (LightRAG / Graphiti) : Maps out entities and relationships in your small dataset. For interconnected rules, troubleshooting steps, or policies, a graph ensures the AI pulls the exact structural context rather than loose paragraphs.
• Deterministic Fallbacks & Routing : For small datasets (under a few hundred documents), a hybrid approach backed by explicit intent classification or hardcoded routing for frequent/critical questions guarantees 100% accuracy on high-value queries.

Top Tool & Framework Recommendations

1. Dify.ai or Voiceflow : Excellent low-code/pro-code platforms that let you tightly control data ingestion, switch on hybrid search, and set precise fallback prompts for small knowledge bases.
2. LlamaIndex (with Recursive Retrieval or Auto-Retrieval) : If you are building custom, LlamaIndex excels at small, structured, and hierarchical data retrieval compared to raw LangChain setups.
3. Qdrant or Chroma + Cohere Rerank : A lightweight vector database paired with an API-based re-ranker gives enterprise-grade precision with minimal infrastructure overhead.

To help narrow down the best architecture, could you share:

• How large is "small" (e.g., 20 documents vs. 500 documents)?
• What format is the data in (FAQs, PDFs, messy text, technical docs)?
• Are you looking for a ready-made SaaS tool or a developer framework to code yourself?

For a small knowledge base where high retrieval accuracy is the top priority, standard vector-based semantic search (RAG) can sometimes overcomplicate things or hallucinate because embeddings struggle with exact keyword matches or precise relational logic on tiny datasets.

Here are the best solutions tailored for high accuracy with limited data:

• Hybrid Search (BM25 + Dense Vectors) : Combines keyword-based search (BM25) with semantic embeddings. Even with a small knowledge base, users often search using exact product names, error codes, or specific terms that pure vector search misses. Tools like Pinecone, Weaviate , or Qdrant support hybrid search out-of-the-box.
• Knowledge Graphs / GraphRAG : If your small knowledge base has interconnected data (e.g., product dependencies, troubleshooting steps, user hierarchies), a lightweight knowledge graph ensures the AI retrieves exact relational facts rather than guessing based on text proximity. Graph databases combined with LLMs excel at this.
• Fine-Tuning a Small Language Model (SLM) : For a truly fixed, small, and static dataset (like an internal company policy or a specific product manual), fine-tuning a small model (like Llama 3 8B or Mistral 7B) using LoRA/QLoRA can internalize the entire knowledge base. This eliminates retrieval misses entirely because the model knows the data rather than searching for it.
• Advanced RAG with Re-ranking (Cohere / BGE-Reranker) : If you stick with standard vector search, add a cross-encoder re-ranker (such as the Cohere Rerank API). It takes the top 20 retrieved chunks from your small database, deeply analyzes their relevance to the specific query, and re-orders them so the LLM only sees the absolute best context. This drastically boosts accuracy.
• Recommendation for Implementation:
  1. Start with a Hybrid Search + Re-ranking pipeline. It is much easier to maintain than a knowledge graph or fine-tuned model and provides immediate, pin-point retrieval accuracy for small document sets.
  2. Ensure your chunking strategy is optimized (e.g., chunking by paragraph or explicit FAQ pairs rather than arbitrary character counts) so the context fed to the LLM is clean.

If you'd like to narrow this down, tell me:

• What format is your data currently in (FAQs, PDFs, a database)?
• Do you need the data to be frequently updated in real-time?

I can recommend the exact tech stack or framework (like LlamaIndex, LangChain, or a specific API) to build this.

For a small, curated support knowledge base, I would not start with a complicated RAG stack. The highest-accuracy setup is usually a small two-stage retrieval system with strong chunking, hybrid retrieval, reranking, and an explicit “I don’t know” threshold.

My recommended architecture

1. Curate the knowledge base first

• Keep only authoritative support content.
• Remove duplicates, outdated instructions, and contradictory versions.
• Split documents by meaning/section, not arbitrary token counts.
• Store metadata such as product, version, feature, region, and document type.

With a small corpus, the quality of the source material can matter more than the choice of vector database.

2. Use hybrid retrieval Use both:

• Semantic/vector search → handles questions phrased differently from the documentation.
• Keyword/BM25 search → catches exact product names, error codes, SKUs, configuration values, etc.

This is particularly useful for support because exact strings and technical terminology are common. Pinecone explicitly recommends full-text search when relevance depends on exact terms and hybrid search when both semantic and lexical signals matter.

3. Retrieve ~10–20 candidates, then rerank Don't send the raw vector-search results directly to the LLM.

A good pipeline is:

User question → hybrid retrieval → top 10–20 → reranker → top 3–5 → LLM

A reranker scores each query/document pair and can substantially improve the ordering of relevant passages.

For a small KB, this is especially attractive because the extra reranking cost is modest.

4. Make the answer model conservative Tell the model:

Answer only from the retrieved support evidence. If the evidence doesn't clearly contain the answer, say you don't have enough information and don't guess.

Even better, put a retrieval confidence threshold in front of generation. If the best evidence is weak, route to “I couldn't find that in the support documentation” rather than letting the LLM improvise.

What I'd actually build

For perhaps hundreds to a few thousand support articles, I'd start with:

You don't necessarily need a heavyweight vector database. With a genuinely small corpus, even a lightweight search stack can work extremely well. The important part is the retrieval pipeline, not scaling infrastructure.

The most important thing: build a test set

Before choosing between vendors, create ~100 representative questions:

• 60 answerable questions
• 20 paraphrased/ambiguous questions
• 20 questions whose answers aren't in the KB

Measure:

Retrieval

• Recall@5
• Recall@10
• MRR / nDCG

Answering

• Correctness
• Citation/evidence correctness
• Abstention accuracy

This lets you compare configurations empirically instead of judging RAG quality by how convincing the chatbot sounds. Recent retrieval research also supports the general pattern that hybrid retrieval followed by neural reranking can outperform single-stage retrieval.

If accuracy is the #1 priority

I'd rank the approaches:

Best:
Hybrid retrieval → reranker → evidence-constrained LLM → abstention

Good/simple:
Dense retrieval → reranker → LLM

Avoid as your first design:
Pure vector search → LLM

And I wouldn't fine-tune the LLM initially. With limited knowledge, fine-tuning teaches behavior; retrieval supplies facts. Your bottleneck is likely getting the right support passage into the context.

If you tell me roughly how many documents/articles you have, their format (FAQ, PDFs, tickets, Markdown, etc.), and whether you need citations, I can recommend a concrete stack (e.g. OpenAI, Pinecone, Elasticsearch, pgvector, or a lightweight local solution) optimized specifically for retrieval accuracy.