# LNMP-LLB Performance Report

## Overview

This document details the performance characteristics of the `lnmp-llb` crate, focusing on:
- ShortForm conversion (Binary ↔ ShortForm)
- Explain Mode encoding
- Prompt Optimization

Benchmarks were conducted using Criterion.rs on a standard development environment.

## Benchmark Results

| Operation | Time (approx) | Throughput (est) |
|-----------|---------------|------------------|
| `shortform_to_binary` (Simple) | ~260 ns | ~3.8M ops/sec |
| `shortform_to_binary` (Arrays) | ~460 ns | ~2.1M ops/sec |
| `binary_to_shortform` (Simple) | ~470 ns | ~2.1M ops/sec |
| `binary_to_shortform` (Arrays) | ~240 ns | ~4.1M ops/sec |
| `explain_encode` | ~1.4 µs | ~700K ops/sec |
| `prompt_optimize` | ~350 ns | ~2.8M ops/sec |

### Key Findings

1.  **High Efficiency**: All core operations are sub-microsecond, with the exception of `explain_encode` which is slightly above 1µs due to verbose text generation.
2.  **Array Performance**: Generic array handling (`IntArray`, `FloatArray`, `BoolArray`) is extremely efficient, often outperforming string-heavy records.
3.  **Prompt Optimization**: The optimizer adds negligible overhead (~350ns), making it suitable for real-time use in LLM pipelines.

## Detailed Analysis

### ShortForm Conversion
- **Binary → ShortForm**: Very fast (~240-470ns). The variation depends on data types; string handling dominates the cost in simple records. Array formatting is highly optimized.
- **ShortForm → Binary**: Also very fast. Parsing overhead is minimal.

### Explain Mode
- **Cost**: ~1.4 µs per record.
- **Reason**: Generates verbose, human-readable text with field names from the semantic dictionary.
- **Verdict**: Acceptable for debugging and "Explain Mode" features where human readability is the priority over raw throughput.

### Prompt Optimization
- **Cost**: ~350 ns per record.
- **Impact**: Extremely low. Can be enabled by default without performance concerns.

## Optimization Recommendations

1.  **String Handling**: String encoding/decoding remains the most expensive part of the pipeline. Future optimizations could focus on zero-copy string handling where possible.
2.  **Explain Mode**: If higher throughput is needed for Explain Mode (unlikely), a streaming encoder could reduce allocation overhead.

## Conclusion

`lnmp-llb` meets and exceeds performance requirements for high-throughput LLM pipelines. The introduction of generic arrays has not introduced any performance regressions and, in fact, offers a highly efficient way to transmit numerical data.
