1 Chaining LLM Reasoning with R: Orchestrating Insights from Data to Expert Reports

Analytics workflows often begin with a question—then move through acquiring raw data, wrangling it for analysis, and applying statistical methods to uncover patterns and insights. These summaries, whether visual or numeric, help illuminate the story behind the data. But what happens when we inject AI into this process?

At dataRecode, we’ve been running lab experiments that embed Large Language Models (LLMs) directly into our R-based analytics workflows to tackle real-world challenges. The results? A noticeable boost in the clarity, nuance, and strategic depth of our outputs. While a domain expert or human-in-the-loop remains essential for validation, the synergy between R and LLMs is transformative.

This blog demonstrates how step-by-step reasoning from LLMs can be orchestrated within R’s powerful data pipeline—linking AI logic with statistical rigor to produce high-impact, decision-ready insights. Why does this matter? Because combining R’s structured data flow with LLM’s narrative reasoning multiplies the depth and quality of problem-solving, helping teams move from raw data to expert-level conclusions with greater speed and confidence.

2 Why Chain LLMs with R? The Power of Orchestration

Chaining prompts—where each LLM response builds on the last—mimics the layered reasoning of expert deliberation. When orchestrated through R, this process unlocks a powerful synergy: R grounds the analysis in structured data, while the LLM adds interpretive depth, policy awareness, and narrative clarity. Together, they produce insights that are not only statistically sound but strategically rich—ideal for decision-making, reporting, and stakeholder engagement.

Take a look below at what each system brings to the table. When combined, their strengths multiply—delivering far greater value than either could achieve alone.

2.1 R’s Strengths

  • Data-First Foundation: R loads and preprocesses datasets, grounding LLM prompts in verifiable facts.
  • Policy Compliance Layer: Before prompting, R can validate inputs against predefined rules (e.g., avoiding biased terms or ensuring proper citations).
  • Sequential Prompting: R generates and feeds prompts in a loop, capturing the full conversation thread for continuity.
  • Tidying for Review: Post-response, R structures LLM outputs into tidy tables or reports, making them human-readable and ready for review.

2.2 LLMs’ Strengths

  • Complex Policy Integration: LLMs can be prompted to apply nuanced, domain-specific rules directly to text or visual analysis outputs, generating dynamic responses that traditional statistical models simply can’t match.
  • Explainable AI: LLMs provide narrative justifications for decisions, such as explaining why an applicant was rejected by referencing a specific policy. They can also generate high-level, human-readable explanations derived straight from the data and its visualizations.
  • Mitigation of Statistical Bias: If a statistical model run in R heavily weights a single variable (a potential source of bias), the LLM can be prompted to scan for mitigating visuals or patterns, introducing the necessary nuance to balance the analysis.

3 Case Study - Chaining LLM Reasoning for Policy-Compliant Reporting

To demonstrate how R and LLMs can work in synchrony, we’ve set up a local LLM farm powered by Ollama, hosting multiple models including Qwen2.5. In this case study, we walk through a chained reasoning workflow where R handles data prep and prompt orchestration, while Qwen2.5 responds step-by-step over a local network. Each prompt builds context, and the final outputs are tidied into a structured tibble—ready for review, reporting, or decision-making. While we use Qwen2.5 throughout for clarity, the architecture supports model-switching per prompt, allowing us to tap into the strongest LLM for each subject matter.

We assume you’re comfortable with R coding and familiar with LLM prompting concepts. This walkthrough focuses on orchestration—not on installing or hosting LLMs. We also assume your Ollama server is running locally with the required models pulled and accessible via REST API.

Click on the arrow to Show code 
# Steps:
# 1. Prep REST API for remote access to specified LLM (e.g., Qwen2.5:7b via Ollama server).
# 2. Load and preprocess data in R.
# 3. Generate sequential prompts in a chained chat, building context step-by-step.
# 4. Tidy outputs into a structured tibble for easy review and reporting.
# Prerequisites: Install and load the 'llm' package (or equivalent for Ollama integration),
# and ensure Ollama is running on your server with the Qwen model pulled.

library(tidyverse)
library(llm)  # For llm_message(), chat(), and Ollama integration

# Step 1: Prep REST API for remote access to specified LLM
ollama_qwen <- function() {
  ollama("qwen2.5:7b", .ollama_server = "http://<your_ip_address>:11434")
}

# Step 2: Load and preprocess data (example: applicant risk data)
data <- read_csv("applicant_data.csv") %>%
  filter(status == "pending") %>%
  mutate(risk_score = pmax(0, pmin(1, risk_score)))  # Normalize scores

# Step 3: Policy validation (e.g., check for bias in risk scoring)
validate_policy <- function(scores) {
  if (var(scores) < 0.01) {
    warning("High uniformity in risk scores—potential bias detected.")
  }
  return(TRUE)
}
validate_policy(data$risk_score)

# Step 4: The chained chat—build sequential prompts with growing context
# Start with an initial question, then chain follow-ups based on prior responses
chat_df <- 
  llm_message("What is electricity generation?") %>%
  chat(ollama_qwen()) %>%
  llm_message("Prompt 1: Based on the data summary [insert summary(data)], analyze risk patterns and flag anomalies.") %>%
  chat(ollama_qwen()) %>%
  llm_message("Prompt 2: Apply policy X to the anomalies above—explain mitigation steps in narrative form.") %>%
  chat(ollama_qwen()) %>%
  llm_message("Prompt 3: Synthesize into a final report outline, including key visuals and justifications.") %>%
  chat(ollama_qwen()) %>%
  llm_message("Prompt 4: Generate a human-readable executive summary, ensuring compliance and bias checks.") %>%
  chat(ollama_qwen()) %>%
  as_tibble() %>%
  filter(role %in% c("user", "assistant")) %>%
  mutate(turn = ceiling(row_number() / 2)) %>%
  pivot_wider(names_from = role, values_from = content) %>%
  transmute(
    question = user,
    answer = assistant
  )

# Step 5: Tidy into report (e.g., export to CSV or render as HTML)
report <- chat_df %>%
  mutate(
    formatted_answer = str_replace_all(answer, "\\n", "<br>"),  # For HTML-friendly line breaks
    timestamp = Sys.time()  # Add audit trail
  )

write_csv(report, "llm_insights_report.csv")
print("Report generated: Chained insights ready for expert review.")
# Optional: View the tidy chat DataFrame
head(report)

4 Takeaway

Chained reasoning ensures that the LLM is not just generating a single response, but thinking strategically through the problem, step-by-step. When expertly paired with R’s data integrity and clean formatting, this process turns disjointed chat responses into a coherent, all-encompassing insight.

Whether you’re a regulator, financier, or decision maker, leveraging analytics powered by chained LLM logic helps you go beyond basic answers—it provides a 360-degree, expert-level perspective that is both explainable and scalable.