Custom RAG Development: Fewer Searches, More Straight Answers
RAG, or Retrieval-Augmented Generation, lets an AI model answer a question based on your documents rather than its internal memory. RAG scans your documents for relevant excerpts, sends them to the language model, and returns an answer with source citations. Facebook AI Research, University College London, and NYU proposed it in a 2020 paper, and it became a popular technique for interfacing language models with corporate knowledge.
PenguinPeak Technologies is a development firm specializing in RAG for businesses across America, India, Australia, New Zealand, and Abu Dhabi. Our team will develop the entire pipeline, including document ingestion, query formulation, result retrieval, and result validation, keeping it updated as your knowledge base changes.
Our RAG Development Services
Step Zero: RAG readiness review.
Our experts analyze your document library for duplicates, outdated documents, and missing pieces, and, if you want, link it to our AI strategy and roadmap service.
The documents come in: ingestion and parsing.
We make connections with your knowledge sources: SharePoint, Google Drive, Confluence, databases, emails, and PDF documents. We run any scanned documents through OCR and layout recognition.
The text is prepared: chunking, metadata, and indexing.
We break the documents into chunks and add source, date, and ownership metadata, then embed and index them in the vector database.
There’s an incoming query: query understanding.
If a query is ambiguous or there are multiple queries, they are rephrased and split up, acronyms are expanded, the language is determined, and the search begins afresh.
The correct passages are identified: Hybrid search and reranking.
Keyword and semantic search run simultaneously; metadata filters the list, and the passages are reranked.
Access is regulated: Access control and governance.
In order to make sure that the model doesn’t have access to restricted information and that all queries are tracked, permissions are checked per document.
An answer is generated and cited: grounded generation.
A language model writes the answer using only the retrieved passages, cites its sources, and claims ignorance if the source material is insufficient. Our model selection and hosting are conducted as part of the LLM development process.
Make Your Documents Work for You.
Send us your documents and the queries your team often asks. We will let you know if a RAG assistant is capable of answering them, and if not, we'll be upfront about it.
Get Your Free RAG Consultation
Why Choose Us for RAG Development: Six Problems We Design Out From Day One
- Good documents, bad chunks. Poorly implemented document chunking splits an answer into two pieces or removes the header that makes it clearer. We validate our document chunks using your actual queries.
- Outdated information, confident answers. Duplicate copies and outdated versions can produce incorrect answers that seem correct. We add versions and dates and delete old copies.
- All users see all documents. Permissions enforced in a prompt can be worked around. We implement permissions in the retrieval layer.
- Tables and scans get lost. Plain text extraction mangles spreadsheets and image-only PDFs. We use layout-aware parsing and OCR, then manually check a sample of the output.
- No way to tell if it works. We build a test set from your real questions and score every change against it.
- Costs creep. Sending too much text to the model drives up the bill. We trim context, cache repeated questions, and report cost per answer.
What Teams Actually Ask a RAG Assistant
Support: “Can the client return a product which has been opened after 30 days?”
So the RAG Assistant finds the returns policy and the exception for defective items, composes a response linking to the article, and sends it after agent verification.
- HR: “How many days of parental leave will I have at the Auckland office?”
It provides an answer based on New Zealand policy, not Australia, and includes the section and the date of the latest update. - Legal: “Which supplier agreements allow for termination with 30 days’ notice?”
It generates a list of relevant provisions, including document titles and clause numbers, letting a lawyer cross-check each clause in seconds rather than hours. - Sales: “Where do we store the data of our clients?”
It answers from approved responses and flags anything it can’t support for a human to review. The same approach applies to teams in healthcare, financial services, legal, education, and eCommerce, each with different data policies.
RAG Questions, Answered in Full
RAG stands for retrieval-augmented generation. When someone asks a question, the system first searches your documents for the most relevant passages. It then gives those passages to a language model, which writes an answer based on them and cites its sources. Because the knowledge lives in your documents, not inside the model, you can update it by changing the documents without retraining anything.
Most business sources include shared files, SharePoint, Google Drive, Confluence, databases, help desks, email archives, and PDFs. We check access and permissions before continuing.
We measure these two components independently. The first component, retrieval, determines whether the right passage appears within the top results of a legitimate query. The second component, generation, checks whether the retrieved passages support the response and answer the question. We measure both components against a test set constructed from your queries. Then, we manually score some live answers to find potential errors that automatic scoring might have missed.
We automatically index new documents and delete old ones. Dates and version numbers will be included with all the documents. In case of conflicting text, the later document is preferred until any human approval. Updates differ by source; for example, price updates happen more often than manual updates.
It greatly reduces the chances but doesn’t eliminate them. We require references, instruct the system to say when it lacks enough references, and test it on questions the documents don’t cover.
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