AI
RAG development
Retrieval-augmented generation is how you keep a model honest against your documents and tickets. The pipeline is chunk, embed, retrieve, generate. The work is making that pipeline fail in boring, visible ways instead of fluent lies.
Key takeaways
Retrieval is the product
If the wrong chunk comes back, the nicest prompt still invents policy. Fix retrieval before you swap models.
Chunking is design
Heading-aware splits, metadata, and overlap beat dumping PDFs into 500-token windows and hoping.
Evals catch fluent errors
Score answer-with-source on a golden set. Demos do not catch the miss that ships on Tuesday.
Fine-tune is a different job
RAG for facts that change. Fine-tune for style or a stable task. Most product work starts with RAG.
What is RAG development?
RAG development is the work of fetching the right pieces of your knowledge, stuffing them into a prompt, and generating an answer that can point at those pieces. The model stays a reasoner. Your corpus stays the source of truth. When retrieval misses, the answer should say so, not improvise a policy.
The pipeline looks simple: split documents into chunks, embed them, store vectors, retrieve on a query, optionally rerank, then generate. Every stage has taste. Bad chunking hides tables. Weak metadata mixes tenants. A query that never gets rewritten misses the doc everyone on the team would have opened.
This is the backbone of a lot of AI agent development. Agents that cannot find the right procedure will still call tools. They will just call them with the wrong story.
How does the chunk, embed, retrieve, generate loop run?
Chunking should respect structure. Headings, sections, and tables want to stay together when they answer one question. Overlap helps when a sentence at a split needs the line before it. Metadata should carry source, date, product, and permission, so you do not retrieve another tenant's contract.
Embeddings turn chunks into vectors. You pick a model, you store vectors next to the metadata, you update them when docs change. Stale indexes are a silent bug. Retrieval is usually nearest-neighbor plus filters. Hybrid search, keyword plus vector, helps on part numbers, names, and error codes that embeddings smear.
Generation should see the retrieved text and a hard instruction to cite or abstain. If you skip citations, you cannot tell a grounded answer from a fluent one. Rerankers help when the first-pass retrieve is noisy. They cost extra. Use them when the first pass is close but not clean.
When should you use RAG instead of fine-tuning?
Use RAG when the facts change, when you must show sources, or when legal will not let you bake customer data into a model. Use fine-tuning when the task is stable and the issue is format or tone, not missing knowledge. Many teams try to fine-tune their way out of a retrieval problem and then wonder why last week's price list never shows up.
Hybrid is allowed. A small tuned model for classification, RAG for the long answer. Do not start there. Start with retrieval on a golden set of questions your users already ask. If that set scores well, you may never need a fine-tune.
If someone sells you a custom model as the first move, ask what happens when the PDF changes tomorrow. RAG updates with the index. A fine-tune updates with a training job.
How do you evaluate RAG and cut hallucinations?
Hallucinations in RAG are usually retrieval misses dressed up as confidence. Measure two things: did we fetch the right chunks, and did the answer stay inside them. Faithfulness scores, citation checks, and a human pass on the ugly cases beat a single thumbs-up in a demo.
Build a golden set from real tickets and real docs, including questions that should return I do not know. Run it on every chunking or model change. Online, watch citation click-through, user corrections, and empty-retrieve rate. Empty retrieve is a gift. It is a chance to abstain instead of invent.
Prompting helps at the margin. It will not save a bad index. Spend the week on parsing, permissions, and query rewriting before you buy another model.
Where has retrieval shown up in real products?
RelayHub needs conversation context and account history so drafts match how that customer actually talks. That is retrieval plus tools, not a generic FAQ bot. RouteMind leans on fleet and load data so advice is about this truck and this lane, not a textbook on logistics.
Neither product is a naked RAG chat. Retrieval sits behind a job: draft a reply, suggest a dispatch move. That is the pattern worth copying. If you only need search, ship search. If you need an answer that acts, RAG is a layer under the agent, not the whole product.
Most of our AI development work looks like this: existing data, existing UI, a retrieval path that can be audited. If that is your situation, how to add AI to an existing product is the companion piece.
Next step
Have a corpus the model must not invent?
Send a sample of the docs, the questions users already ask, and who is allowed to see what. We will say if RAG is the right layer.
Talk to Algo VortexRelated in this cluster
- AI agent developmentAn AI agent is software that can take steps toward a job, not only answer a question. This guide covers tools, memory, RAG, MCP, human review, and what it takes to run one in production.
- Model Context ProtocolMCP is a standard way for models and agents to reach tools and data. Instead of a custom plugin for every vendor, you expose capabilities once and let compatible clients call them.
- How to add AI to an existing productMost useful AI work is not a new app. It is an integration layer on software you already run: your auth, your data, your UI, a model in the middle, and a way to turn it off.
Related capabilities
Related case studies
Live products where this kind of work showed up in the build.
Twilio + OpenAI inbox automation
RelayHub started from a blunt observation: phone and chat should not live in separate tools. Sales and support kept losing the thread when a caller switched to SMS or a chat widget. The brief was one shared inbox. Twilio traffic and digital messages land together. AI clears the routine work so people only jump in when judgment matters. Teams also needed to steer the assistant without shipping a new build every time the script changed. Admin-controlled prompts per contact group were in the brief from day one. File digests mattered too. Long PDFs and call notes piled up unread. The product needed a path from upload to a short summary the whole group could scan before the next shift. Nobody on the project believed every reply should be fully automated. Refund fights, tone-sensitive replies, and messy exceptions still need a human. RelayHub uses OpenAI to draft, summarize, and clear the easy queue so senior staff spend time on work that actually needs them.
RouteMind: fleet dispatch that cuts empty miles
AI fleet advisor + live load board
RouteMind exists so shippers and carriers can see loads, capacity, and routes in one place. Dispatch should cost less time and fewer wasted miles. The product pairs a live load board with an AI Fleet Advisor. Planners match freight to available trucks and compare paths with real map data instead of gut feel. Empty miles and stale boards were the business pain. When capacity is a guess, trucks deadhead and fuel burns for no revenue. Status, distance, and advisor guidance had to show up in the tools dispatchers already live in. Another spreadsheet export at the end of the shift was not going to cut it. Dispatchers needed advice that respected current capacity, not a generic logistics chatbot. The Fleet Advisor had to read live loads and vehicle state, then suggest moves a planner could accept or reject in the same UI. RouteMind was never meant to replace judgment. It was meant to cut the time spent assembling the picture before judgment starts.
Questions
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