Reasoning Effort Replaces Temperature as Primary Lever

As of January 2026, the prompt-engineering landscape has shifted fundamentally. The primary lever is now reasoning_effort (Low/Medium/High), displacing temperature as the dominant tuning parameter.

Increasing reasoning_effort burns more tokens on hidden chain-of-thought reasoning but improves logic accuracy for complex problems. API responses now separate content tokens (visible) from reasoning_tokens (billed separately), giving practitioners direct visibility into the trade-off between cost and reasoning depth.

Specialized Techniques Outperform Generalists

Chain-of-Symbol (CoS) outperforms Chain-of-Thought (CoT) for spatial and planning tasks, offering a more targeted approach when domain-specific reasoning is required.

DSPy 3.0: Automated Prompt Compilation

DSPy 3.0 introduces automatic prompt compilation. Users define a Signature (Input → Output), provide 10 examples, and DSPy optimises the prompt for the target model—eliminating manual prompt iteration for many workflows.

Few-Shot Prompting: The Sweet Spot

The optimal number of few-shot examples falls between 2 and 5. Fewer than two often fails to establish a clear pattern, while more than five rarely improves performance and increases token costs and latency.

TextGrad: Backpropagation for Prompts

The Stanford DSPy team followed up with TextGrad, a system that uses backpropagation and text-based feedback to evaluate LLM output and improve prompts—extending optimization beyond manual tuning.

Source: Digital Applied