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Inference-time optimizations are powerful techniques that can significantly enhance the performance of your LLM applications without the need for model fine-tuning. This guide will explore two key strategies implemented as variant types in TensorZero: Best-of-N (BoN) sampling and Dynamic In-Context Learning (DICL). Best-of-N sampling generates multiple response candidates and selects the best one using an evaluator model, while Dynamic In-Context Learning enhances context by incorporating relevant historical examples into the prompt. Both techniques can lead to improved response quality and consistency in your LLM applications.

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Best-of-N Sampling

Inference-Time Optimization: Best-of-N Sampling Best-of-N (BoN) sampling is an inference-time optimization strategy that can significantly improve the quality of your LLM outputs. Here’s how it works:
  1. Generate multiple response candidates using one or more variants (i.e. possibly using different models and prompts)
  2. Use an evaluator model to select the best response from these candidates
  3. Return the selected response as the final output
This approach allows you to leverage multiple prompts or variants to increase the likelihood of getting a high-quality response. It’s particularly useful when you want to benefit from an ensemble of variants or reduce the impact of occasional bad generations. Best-of-N sampling is also commonly referred to as rejection sampling in some contexts.
TensorZero also supports a similar inference-time strategy called Mixture-of-N Sampling.
To use BoN sampling in TensorZero, you need to configure a variant with the experimental_best_of_n type. Here’s a simple example configuration:
tensorzero.toml
[functions.draft_email.variants.promptA]
type = "chat_completion"
model = "gpt-4o-mini"
user_template = "functions/draft_email/promptA/user.minijinja"

[functions.draft_email.variants.promptB]
type = "chat_completion"
model = "gpt-4o-mini"
user_template = "functions/draft_email/promptB/user.minijinja"

[functions.draft_email.variants.best_of_n]
type = "experimental_best_of_n"
candidates = ["promptA", "promptA", "promptB"]

[functions.draft_email.variants.best_of_n.evaluator]
model = "gpt-4o-mini"
user_template = "functions/draft_email/best_of_n/user.minijinja"

[functions.draft_email.experimentation]
type = "uniform"
candidate_variants = ["best_of_n"]  # so we don't sample `promptA` or `promptB` directly
In this configuration:
  • We define a best_of_n variant that uses two different variants (promptA and promptB) to generate candidates. It generates two candidates using promptA and one candidate using promptB.
  • The evaluator block specifies the model and instructions for selecting the best response.
You should define the evaluator model as if it were solving the problem (not judging the quality of the candidates). TensorZero will automatically make the necessary prompt modifications to evaluate the candidates.
Read more about the experimental_best_of_n variant type in Configuration Reference.
We also provide a complete runnable example:Improving LLM Chess Ability with Best/Mixture-of-N SamplingThis example showcases how best-of-N sampling can significantly enhance an LLM’s chess-playing abilities by selecting the most promising moves from multiple generated options.

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Dynamic In-Context Learning (DICL)

Inference-Time Optimization: Dynamic In-Context Learning Dynamic In-Context Learning (DICL) is an inference-time optimization that improves LLM performance by incorporating relevant historical examples into your prompt. Instead of incorporating static examples manually in your prompts, DICL selects the most relevant examples at inference time. See the Dynamic In-Context Learning (DICL) Guide to learn more.
We also provide a complete runnable example:Optimizing Data Extraction (NER) with TensorZeroThis example demonstrates how Dynamic In-Context Learning (DICL) can enhance Named Entity Recognition (NER) performance by leveraging relevant historical examples to improve data extraction accuracy and consistency without having to fine-tune a model.

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Mixture-of-N Sampling

Inference-Time Optimization: Mixture-of-N Sampling Mixture-of-N (MoN) sampling is an inference-time optimization strategy that can significantly improve the quality of your LLM outputs. Here’s how it works:
  1. Generate multiple response candidates using one or more variants (i.e. possibly using different models and prompts)
  2. Use a fuser model to combine the candidates into a single response
  3. Return the combined response as the final output
This approach allows you to leverage multiple prompts or variants to increase the likelihood of getting a high-quality response. It’s particularly useful when you want to benefit from an ensemble of variants or reduce the impact of occasional bad generations.
TensorZero also supports a similar inference-time strategy called Best-of-N Sampling.
To use MoN sampling in TensorZero, you need to configure a variant with the experimental_mixture_of_n type. Here’s a simple example configuration:
tensorzero.toml
[functions.draft_email.variants.promptA]
type = "chat_completion"
model = "gpt-4o-mini"
user_template = "functions/draft_email/promptA/user.minijinja"

[functions.draft_email.variants.promptB]
type = "chat_completion"
model = "gpt-4o-mini"
user_template = "functions/draft_email/promptB/user.minijinja"

[functions.draft_email.variants.mixture_of_n]
type = "experimental_mixture_of_n"
candidates = ["promptA", "promptA", "promptB"]

[functions.draft_email.variants.mixture_of_n.fuser]
model = "gpt-4o-mini"
user_template = "functions/draft_email/mixture_of_n/user.minijinja"

[functions.draft_email.experimentation]
type = "uniform"
candidate_variants = ["mixture_of_n"]  # so we don't sample `promptA` or `promptB` directly
In this configuration:
  • We define a mixture_of_n variant that uses two different variants (promptA and promptB) to generate candidates. It generates two candidates using promptA and one candidate using promptB.
  • The fuser block specifies the model and instructions for combining the candidates into a single response.
You should define the fuser model as if it were solving the problem (not judging the quality of the candidates). TensorZero will automatically make the necessary prompt modifications to combine the candidates.
Read more about the experimental_mixture_of_n variant type in Configuration Reference.
We also provide a complete runnable example:Improving LLM Chess Ability with Best/Mixture-of-N SamplingThis example showcases how Mixture-of-N sampling can significantly enhance an LLM’s chess-playing abilities by selecting the most promising moves from multiple generated options.