Tailoring Prompts for Different LLMs
While general prompt engineering principles apply broadly, achieving optimal performance often requires tailoring strategies to the specific characteristics of the Large Language Model being used. Factors like whether the model is a base model or instruction-tuned, its size and inherent capabilities, and even specific architectural nuances or training data idiosyncrasies can influence how it responds to different prompting techniques.
Base Models vs. Instruction-Tuned Models
A fundamental distinction exists between base LLMs and their instruction-tuned counterparts, significantly impacting effective prompting strategies.
Base Models
(e.g., foundational GPT, Llama before instruct-tuning) These models are primarily trained to predict the next token. They possess broad knowledge but aren't optimized for following commands directly.47 They might continue your prompt text instead of treating it as an instruction.47
Prompting Strategies:
- Relies heavily on **few-shot learning** (providing examples).46
- Structure prompts as completions (e.g., "Q: ... A: ...").145
- Zero-shot instructions may be poorly executed.47
- Requires careful structuring and clarity.
Instruction-Tuned Models
(e.g., ChatGPT series, Claude, Gemini, Llama-Instruct, Mistral-Instruct) These undergo additional fine-tuning (SFT, RLHF/RLAIF) on instruction-response datasets.47 They are much better at understanding and following zero-shot instructions.53
Prompting Strategies:
- **Zero-shot prompting** is effective for many tasks.53
- Direct instructions, role prompting, format requests work well.53
- Few-shot examples still useful for complex tasks.53
- Advanced techniques (CoT) are effective.146
- Focus shifts from *showing* (few-shot) to *telling*.
This distinction is fundamental: instruction tuning essentially bakes in some level of prompt-following ability, reducing the burden on the prompt engineer for many common tasks.47 However, even instruction-tuned models benefit from clear, specific, and well-structured prompts, especially as task complexity increases.
Impact of Model Size and Capability
The size and inherent capability of an LLM are major factors influencing which prompting techniques are effective and necessary.
Smaller Models
(e.g., < 10B parameters, sometimes up to ~70B like Mistral-7B, Llama 3 8B). Often have limitations in complex reasoning and following intricate instructions.146
Prompting Strategies:
- Simpler prompts are often more effective.
- Complex reasoning (CoT) may offer limited benefit or degrade performance.84
- Break down tasks into smaller, sequential prompts (prompt chaining).11
- Few-shot examples can be crucial.
- More prompt optimization effort may be needed.146
- Self-Consistency, ToT, GoT generally unsuitable.
Larger Models
(e.g., > 70B-100B+ parameters like GPT-4 class, Claude 3 Opus, Llama 3 70B/405B). Exhibit "emergent abilities" like stronger reasoning and instruction following.35, 4
Prompting Strategies:
- Respond well to zero-shot instructions.
- Significantly benefit from CoT for complex reasoning.84
- Suitable for Self-Consistency, ReAct, ToT, GoT (consider cost).4
- Prompt optimization for conciseness is important due to cost.11
- Effectively utilize role prompting and structured prompts.
The scale of the model is arguably the most significant factor determining the applicability of advanced reasoning prompts like CoT, ToT, and GoT. These techniques often rely on emergent reasoning capabilities that are typically only present in very large models.84 Attempting complex reasoning prompts with smaller models may lead to poor or nonsensical results.
Strategies for Specific Models
Beyond general size and tuning distinctions, specific model families often have unique characteristics and recommended prompting practices: