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How to Fine-Tune an LLM for Software Architecture Trade-Off Reasoning

Reasoning through the trade-offs between competing architectural options for a given context — distinct from producing a single system design.

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What can this kind of fine-tuning teach an LLM?

A general-purpose model may already know some of this subject. Fine-tuning serves a different purpose: it repeatedly demonstrates the kinds of requests, response patterns, terminology, decisions and workflows you want the model to handle more consistently.

For Architecture Trade-off Reasoning, the dataset focuses on reasoning through the trade-offs between competing architectural options for a given context — distinct from producing a single system design. Fine-tuning shapes recurring model behavior; it does not turn the model into a live database or guarantee correctness on every new case.

What training data should cover

Useful supervised fine-tuning data should resemble the work you expect the model to perform. This Rebiha dataset's domain definition is organized around 12 coverage areas:

How Rebiha prepared the training material

This page contains 489 instruction-response pairs from the existing Rebiha dataset. Its domain definition was designed around the coverage areas above so the training material spans multiple realistic tasks rather than one narrow prompt pattern.

Rebiha's broader dataset workflow uses structured quality review to help identify problems such as contradictions, unsupported claims, unsupported assumptions, calculation errors and other weak training examples. You can inspect real examples on this page before deciding whether the dataset fits your model and use case.

What is in this dataset

489 instruction-response pairs covering reasoning through the trade-offs between competing architectural options for a given context — distinct from producing a single system design. Each pair is a realistic instruction with a full written response, in the JSONL format used for supervised fine-tuning. No synthetic filler, no duplicated rows.

Example pairs

Real rows from the dataset, shown in full.

We are a 5-person startup building an internal tool for our own operations team. We expect maybe 50 concurrent users, and the feature set will grow but is currently 3 core workflows. One engineer argues we should start with microservices so we don't re-architect later. Another says monolith. How should we decide, given our team size and stage?

For a 5-person team with 50 concurrent users, the trade-off is not about scalability but about cognitive load and deployment complexity. Microservices would split a small codebase into many deployable units, requiring service discovery, distributed tracing, and inter-service communication patterns—all overhead that consumes your limited engineering hours. The monolith lets all 5 engineers work in one codebase with a single deploy pipeline, which is faster to iterate on. The risk of 're-architecting later' is real but manageable: enforce clear module boundaries inside the monolith (e.g., separate packages for each workflow, no cross-imports except through defined interfaces). If you later need to split, those boundaries become the service seams. The only scenario where microservices win at this stage is if you have a hard requirement for independent scaling of different workloads, which is unlikely at 50 users. So: choose the monolith, but design for modularity. Revisit only when your team exceeds roughly 15 engineers or you hit a genuine scaling bottleneck—not before.

We're building a multi-tenant SaaS where each tenant can define custom fields on their records. The access pattern is: every query filters by tenant_id, then by a set of standard columns, but custom fields are only ever read/written as a whole object per record—never filtered or indexed individually. We're torn between PostgreSQL with JSONB and MongoDB. Which is the better fit and why?

Given your access pattern—always filtering by tenant_id and standard columns, and treating custom fields as opaque blobs—PostgreSQL with JSONB is the better choice. Here's the trade-off reasoning: MongoDB would give you flexible schema natively, but its query planner is weaker for the tenant_id + standard-column filtering that forms your hot path, especially if you need secondary indexes on standard columns combined with the tenant filter. PostgreSQL lets you create a composite index on (tenant_id, standard_col) and still store the custom fields as JSONB. Since you never filter on custom fields, you don't need GIN indexes on JSONB, so the JSONB type is just a storage container. Also, PostgreSQL gives you transactional integrity across standard columns and custom fields in the same record, which MongoDB doesn't offer without multi-document transactions (which are slower and more complex). The only advantage MongoDB gives is if you later need to query into custom fields, but you explicitly said you don't. So: use PostgreSQL with a table having tenant_id, standard columns, and a custom_fields JSONB column. This avoids the operational cost of running a second database and gives you stronger consistency guarantees for free.

Our engineering team is 6 people, all full-stack, and we maintain a legacy Java monolith. We need a feature to send scheduled email digests to users. We found a mature third-party API that does exactly this (scheduling, template management, retry logic) for $500/month. Building it ourselves would take roughly 3 engineer-weeks and then ongoing maintenance. But our CTO says 'build' because we already have the infrastructure. What's the right call?

The trade-off here is between a one-time capital cost of 3 engineer-weeks vs a recurring $500/month operating cost. First, quantify the engineering cost: 3 engineer-weeks for a 6-person team means roughly 12% of your total capacity for that month is gone—that's a significant opportunity cost when you likely have other feature work. But the CTO's point about existing infrastructure matters: if you already run a cron scheduler, have a queue system, and have email delivery configured, the build cost might drop to 1.5 weeks, and the marginal maintenance might be low. However, the third-party API's retry logic, template versioning, and deliverability monitoring are not trivial to replicate—they are exactly the kind of hidden complexity that eats time. Also, $500/month is $6k/year, which is less than the fully-loaded cost of even half an engineer-week for most teams. Given your team is only 6 people, the buy option preserves scarce human capacity for core product differentiators, not commodity email digests. Unless you have a hard data-privacy requirement preventing you from sending data to the vendor, buy. If you absolutely must keep data on-prem, then build, but scope it tightly: use your existing queue for scheduling and delegate only the actual sending to a lower-level email API—don't build the scheduling framework from scratch.

Who this is for

Builders who want their fine-tuned model to reliably perform this specific capability, as part of a broader assistant or agent.

Which models this works with

The dataset is plain JSONL, so it works with any instruction-tuned open model. On Rebiha you can train it directly on:

Training uses LoRA, or QLoRA on a 4-bit base for larger models. Your base model's weights are never modified — training produces a separate adapter.

How it works

  1. Pick this dataset and a base model.
  2. Rebiha runs the job on a managed GPU. You set up nothing.
  3. Download your model: a ready-to-run GGUF, plus developer assets (adapter, tokenizer, configs).

Fine-tuning or RAG?

Use RAG when the model needs facts that change — your prices, your documents, today's inventory. Use fine-tuning when you want consistent behavior and structure without prompting for it every time. This dataset teaches the shape of this capability, not facts to look up.

How managed fine-tuning works

  1. Inspect the dataset examples and choose a compatible base model.
  2. Rebiha runs the training job on managed GPU infrastructure using LoRA or QLoRA as appropriate.
  3. Download portable model assets for your own workflow, including the available ready-to-run and developer outputs for the selected training path.

Fine-tuning or RAG?

Use fine-tuning when the objective is recurring behavior: response structure, domain patterns, terminology, workflow, or how the model approaches a class of requests. Use RAG when answers depend on changing or proprietary facts that should be retrieved at request time. The two approaches can also work together.

Know what goes in. Own what comes out.

You can inspect real examples before training, choose the model you want to adapt, and keep portable model assets when training is complete.

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