Claude Code for LaTeX Document Workflow 2026

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The Workflow

Use Claude Code to write, edit, and manage LaTeX documents for academic papers, theses, technical reports, and presentations. Claude Code handles complex formatting, bibliography management, equation typesetting, and compilation pipeline automation.

Expected time: 10 minutes setup Prerequisites: TeX Live 2024+ or MiKTeX, Claude Code CLI installed, latexmk

Setup

1. Install LaTeX and Tools

# macOS
brew install --cask mactex-no-gui
brew install latexmk

# Ubuntu/Debian
sudo apt install texlive-full latexmk

# Verify
pdflatex --version
latexmk --version
biber --version

2. Create CLAUDE.md for LaTeX Projects

# Project: Research Paper

LaTeX document using ACM sigconf template.

## Conventions
- Use biblatex with biber backend (not bibtex)
- All figures in figures/ directory as PDF or PNG
- Tables use booktabs package (no vertical lines)
- Code listings use minted package
- Cross-references: \cref{} from cleveref package
- Citations: \textcite{} for narrative, \parencite{} for parenthetical
- One sentence per line in source (for better git diffs)
- Compile with: latexmk -pdf -shell-escape main.tex

3. Create Compilation Script

cat > build.sh << 'EOF'
#!/bin/bash
set -e

echo "Building LaTeX document..."
latexmk -pdf -shell-escape -interaction=nonstopmode main.tex

if [ $? -eq 0 ]; then
    echo "Success: main.pdf generated ($(du -h main.pdf | cut -f1))"
else
    echo "Build failed. Check main.log for errors."
    tail -20 main.log
    exit 1
fi
EOF
chmod +x build.sh

4. Verify

claude --print "What LaTeX packages are loaded in main.tex?"
# Expected output:
# Lists the packages from your document preamble

Usage Example

Generate a complete research paper structure with Claude Code:

claude

> Create a LaTeX paper structure for a computer science research paper:
> - ACM sigconf format
> - Title: "Efficient Caching Strategies for Distributed LLM Inference"
> - Sections: Abstract, Introduction, Background, Methodology,
>   Evaluation, Related Work, Conclusion
> - biblatex with biber
> - Include a Makefile for compilation

Claude Code generates:

% main.tex
\documentclass[sigconf,nonacm]{acmart}

\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{booktabs}
\usepackage{graphicx}
\usepackage{amsmath}
\usepackage{cleveref}
\usepackage[style=acmauthoryear,backend=biber]{biblatex}
\addbibresource{references.bib}

\title{Efficient Caching Strategies for Distributed LLM Inference}

\author{Jane Smith}
\affiliation{%
  \institution{University of Example}
  \city{San Francisco}
  \state{California}
  \country{USA}
}
\email{[email protected]}

\begin{abstract}
We present a novel caching framework for distributed large language
model inference that reduces redundant computation by 47\%
while maintaining response quality.
Our approach combines semantic similarity caching with
prefix-aware key-value cache sharing across inference nodes.
\end{abstract}

\begin{document}
\maketitle

\section{Introduction}
\label{sec:introduction}

Large language model inference at scale presents significant
computational challenges.
Each request requires processing through billions of parameters,
making caching strategies essential for practical deployment.

\section{Background}
\label{sec:background}

\subsection{KV-Cache in Transformer Models}

The key-value cache stores intermediate attention computations
to avoid redundant work during autoregressive generation
\parencite{vaswani2017attention}.
For a model with $L$ layers and head dimension $d_h$,
the cache size per token is:

\begin{equation}
  \text{Cache}_{size} = 2 \times L \times n_h \times d_h \times \text{sizeof(dtype)}
  \label{eq:cache-size}
\end{equation}

where $n_h$ is the number of attention heads.

\subsection{Semantic Caching}

Semantic caching groups requests by embedding similarity
rather than exact string matching.
Given an embedding function $f: \mathcal{T} \to \mathbb{R}^d$,
two prompts $p_1, p_2$ share a cache entry when:

\begin{equation}
  \text{sim}(f(p_1), f(p_2)) > \tau
  \label{eq:similarity}
\end{equation}

\section{Methodology}
\label{sec:methodology}

Our framework operates in three stages, as shown in \cref{fig:architecture}.

\begin{figure}[t]
  \centering
  \includegraphics[width=\columnwidth]{figures/architecture.pdf}
  \caption{System architecture showing the three-stage caching pipeline.}
  \label{fig:architecture}
\end{figure}

\section{Evaluation}
\label{sec:evaluation}

\begin{table}[t]
  \centering
  \caption{Inference latency comparison (ms) across caching strategies.}
  \label{tab:latency}
  \begin{tabular}{lrrr}
    \toprule
    Strategy & P50 & P95 & P99 \\
    \midrule
    No cache       & 2,340 & 4,120 & 6,890 \\
    Exact match    & 1,890 & 3,450 & 5,230 \\
    Semantic (ours) &   980 & 2,100 & 3,410 \\
    \bottomrule
  \end{tabular}
\end{table}

The results in \cref{tab:latency} demonstrate that our semantic
caching approach reduces median latency by 58\% compared to
no caching and 48\% compared to exact-match caching.

\section{Related Work}
\label{sec:related}

\textcite{pope2023efficiently} introduced the concept of
paged attention for efficient KV-cache management.

\section{Conclusion}
\label{sec:conclusion}

We presented a semantic caching framework that significantly
reduces inference latency for distributed LLM serving.

\printbibliography

\end{document}

# Makefile
.PHONY: all clean watch

all: main.pdf

main.pdf: main.tex references.bib
	latexmk -pdf -shell-escape -interaction=nonstopmode main.tex

clean:
	latexmk -C
	rm -f *.bbl *.run.xml

watch:
	latexmk -pdf -shell-escape -pvc main.tex

Continue editing with Claude Code:

> Add a "Limitations" subsection to the Conclusion. Mention that
> our approach has higher memory overhead and doesn't handle
> streaming responses. Include the equation for memory overhead.

Common Issues

Why This Matters

Researchers spend 30-40% of paper-writing time on LaTeX formatting. Claude Code handles the boilerplate, equation typesetting, and table formatting so you can focus on the content and analysis.