| Technique | Summary | Advantages | When to use | Requirements (compute, data) | Complexity |
|---|---|---|---|---|---|
| Training from Scratch | Train a new model using your own dataset. | Full control over the model; tailored to your problem. | When no suitable pretrained model exists, or you're building a model for a unique task. | Large datasets and significant compute (typically GPUs). | High |
| Use a Pretrained Model (out-of-the-box) | Use an existing pretrained model without modifying its weights. | Fastest and simplest approach; no training required; benefits from the capabilities learned during pretraining. | When the model already performs well for the task and additional customisation is unnecessary. | Access to a pretrained model and inference compute. No additional training data required. | Low |
| Quantization | Reduce numerical precision of model weights (e.g. FP16 → INT8), usually after training. | Smaller models, lower memory usage, faster inference. | Deploying models on limited hardware or reducing inference cost. | No training data; minimal compute. | Low-Medium |
| Full Fine-tuning | Continue training a pretrained model on your own data (updating all the parameters). | Higher accuracy than prompting alone; learns domain-specific behaviour. | You have labelled data and need to adapt a pretrained model. | Labelled dataset and moderate-to-high compute (usually GPUs). | High |
| LoRA / PEFT | Fine-tune only a small subset of model parameters. | Much cheaper and faster than full fine-tuning; small adapter files. | You need to customise a large model with limited resources. | Small labelled dataset and moderate compute. | Medium–High |
| Technique | Summary | Advantages | When to use | Requirements (compute, data) | Complexity |
|---|---|---|---|---|---|
| Prompt Engineering | Design solid instructions and examples to guide model behaviour without changing model weights. | Fast, simple, no retraining required. | The base model already performs reasonably well. | No additional data or compute. | Low |
| Structured Outputs | Constrain responses to a predefined schema (e.g. JSON). | Reliable, machine-readable outputs; easier integration. | Building APIs, automations, or AI applications. | No additional data or compute. | Low |
| Tool / Function Calling | Allow the model to invoke external tools, APIs, or databases. | Access to real-time data and external capabilities. | The model needs to perform actions or retrieve live information. | APIs, external services, and programming effort. | Medium |
| RAG (Retrieval-Augmented Generation) | Retrieve relevant documents and provide them as context to the model. | Uses up-to-date, private, or domain-specific knowledge without retraining. | Answers depend on documents, manuals, PDFs, or internal knowledge bases. | Knowledge base, embeddings, vector database, and indexing pipeline. | Medium–High |
| Model Routing | Select the most appropriate model for each request. | Reduces cost while maintaining quality. | Different tasks have different quality, latency, or cost requirements. | Access to multiple models and routing logic. | Medium |
| Agentic Systems / Workflows | Combine planning, memory, and tool use to solve multi-step tasks. | Automates complex workflows and decision-making. | Tasks require multiple reasoning or tool-calling steps. | Multiple tools, orchestration framework, and evaluation. | High |