Choose an LLM architecture, define its evaluation, and plan production operations.
Language models can draft, classify, extract, and answer questions, but selecting a model is only one part of delivering a useful application. Define the task, evidence requirements, and acceptable failure modes before choosing training or retrieval.
This guide hub connects model adaptation, retrieval, prompting, and production operations. Start with the decision checklist below, then use the linked resources for the area where your baseline is failing.
Quality, cost, and latency depend on the model, data, workload, and evaluation method. Test changes against representative cases and keep a record of known limitations before expanding deployment.
Explore adapter and full fine-tuning methods, then evaluate task quality on held-out examples.
Build retrieval-augmented generation systems with vector databases, embedding models, and retrieval strategies for accurate, context-aware responses.
Master few-shot learning, chain-of-thought prompting, and advanced techniques to maximize LLM performance without model retraining.
Measure inference latency and cost, evaluate quantization, and test caching under your expected load.
The linked articles cover individual implementation topics. Check their assumptions and source dates against your intended model and environment before applying a technique.
By TensorBlue Team · Updated
Start with a small evaluation set drawn from the actual task. Include ordinary requests, ambiguous inputs, missing evidence, and requests the application should decline. Record the expected output, who can judge it, and the cost of an incorrect response. Run a simple prompt baseline before adding retrieval or training; otherwise you cannot tell which added component helped.
Use explicit instructions, examples, and output validation when failures concern task interpretation or response structure. Keep the model, test cases, and generation settings fixed while comparing prompts. A prompt change that fixes one example may still regress another.
Use retrieval when answers need current documents, citations, or access-controlled information. Test retrieval separately from generation: did the correct passage reach the model, and did the answer use it faithfully? Filter permissions before providing context and test what happens when the source is absent.
Consider training when you have representative examples and a recurring behaviour gap that simpler changes have not resolved. Separate training and evaluation data. Fine-tuning and retrieval can coexist; training is not a dependable substitute for fetching newly changed facts.
For adapter training, LoRA freezes the base weights and learns smaller update matrices. See Hugging Face’s LoRA explanation. This describes the method, not a guaranteed accuracy gain for your workload. Our architecture checklist is editorial guidance; validate it against your data and operating constraints.
For serving, benchmark the actual model and hardware before choosing quantization or caching. Include long inputs, concurrent requests, cache misses, and output validation. Memory savings alone do not establish acceptable answer quality or throughput.
Step-by-step guide: LoRA, QLoRA, full fine-tuning for GPT, Llama, Mistral.
Compare compression methods and measure memory, latency, and task-quality trade-offs.
Plan training data, held-out evaluation, and deployment for your specific task.
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