Fine-Tuning | Prompt Eng. | Instruction Tuning

Understanding the Customization Spectrum

As large language models (LLMs) mature, customizing them has gone from "nice to have" to business-critical. But with so many options—prompt engineering, instruction tuning, fine-tuning, LoRA, QLoRA, adapters—the decision tree can quickly become overwhelming.

Let’s demystify the landscape.

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What Are They, Exactly?

MethodDefinitionPrompt EngineeringDesigning precise, structured inputs to steer LLM output behaviorInstruction TuningTraining models on a variety of instructions to generalize across tasksFine-TuningRe-training parts (or all) of the base model on custom labeled dataLoRA / QLoRALightweight fine-tuning via low-rank adapters—cheap and memory-efficient

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Key Tradeoffs to Consider

FactorPromptingInstruction TuningFull Fine-TuningLoRA / QLoRACostNoneHighVery HighLowLatencyLowMediumMediumLowData NeededNone1K–10K examples50K+1K–5KCustom BehaviorShallowGeneralizedDeepTargetedDev ExperienceEasiestHarderComplexModerate

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What Should You Use—and When?

Use Prompt Engineering when:

• You need speed and don’t control the model
• Use cases are simple or exploratory
• Example: marketing copywriter, simple chatbot, internal search

Use Instruction Tuning when:

• You want a flexible generalist
• You're building a developer platform or agentic tool
• Example: a model that can summarize, translate, and classify with equal ease

Use Fine-Tuning / LoRA when:

• You need deep domain alignment or structured output
• You have control of infrastructure or use open-source models
• Example: legal document summarizer, customer support LLM, medical QA bot

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“Don’t reach for a hammer when a prompt will do the job.”
— NovaLuna Labs, Applied AI Team

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What’s Emerging?

• Mixtral Adapters: Modular, parameter-efficient tuning method used in mixture-of-experts models.
• QLoRA: Memory-efficient fine-tuning at full 16–32-bit performance, great for edge deployment.
• Prompt Compression: Agents now learn to shorten prompts, cutting cost and latency.

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Final Thoughts

There’s no one-size-fits-all. Your LLM customization method should align with:

• The complexity of your task
• Your budget and infrastructure
• Your team’s technical capability

At NovaLuna, we often recommend:

• Prompt engineering → for prototypes
• LoRA → for early production
• Full fine-tuning → for scale-critical, regulated use cases

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Make your customization strategy deliberate—not expensive.

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