Prompt Chaining and Sequencing in Prompt Engineering

Prompt chaining and sequencing are prompt-engineering techniques in which a complex task is decomposed into a structured sequence of prompts, where the output of one step becomes input or context for the next ¹²⁶. The primary purpose is to improve reliability, controllability, and transparency of large language model (LLM) workflows by guiding the model through intermediate subtasks rather than asking for a final answer in one shot ¹⁶. These techniques are increasingly important as LLMs are integrated into multi-step applications such as research assistants, data pipelines, and agents, where stepwise reasoning, validation, and orchestration are critical for robust performance ²⁵⁶. Prompt chaining and sequencing thus form a core part of LLM application design, bridging raw model capability and production-grade systems ⁴⁵⁶.

Overview

Prompt chaining and sequencing emerged as a response to fundamental limitations in how LLMs handle complex, multi-faceted tasks. While LLMs are powerful probabilistic sequence models, they can struggle with long, underspecified, or multi-objective prompts that attempt to accomplish too much in a single interaction ¹². By breaking tasks into subtasks, chaining leverages the model’s strength in local coherence and contextual recall over shorter spans, while letting developers validate, constrain, or correct each step ¹²⁶.

The practice evolved from early observations that LLMs performed better when guided through intermediate reasoning steps rather than being asked to produce final answers directly. Research on multi-step prompting techniques such as least-to-most prompting and tool-using agents demonstrated substantial gains on complex reasoning and decision-making benchmarks when tasks were decomposed ⁶⁸. This led to the formalization of prompt chaining as a structured methodology, distinct from casual multi-turn chat: chains are planned, structured, and usually implemented in code or orchestration frameworks, often with branching or conditional logic ¹⁴⁵.

As LLM applications have scaled into production environments, prompt chaining has become essential for debuggability, modularity, and safety, enabling organizations to treat LLM behavior more like an inspectable pipeline they can govern ⁴⁵⁶. Today, prompt chaining underpins many real-world systems including question-answering over long documents, staged code generation and testing, data cleaning pipelines, and retrieval-augmented agents that perform search, analysis, and synthesis in multiple passes ²⁴⁶.

Key Concepts

Task Decomposition

Task decomposition is the practice of breaking down a complex objective into a series of smaller, well-defined subtasks that can be addressed sequentially ²⁶⁸. Each subtask represents a discrete operation such as extraction, transformation, reasoning, or formatting, with clear inputs, outputs, and success criteria.

Example: A financial analyst building an earnings report automation system decomposes the task into five distinct steps: (1) extract key financial metrics from quarterly filings using a prompt that outputs structured JSON with revenue, profit, and growth fields; (2) calculate year-over-year percentage changes using those extracted values; (3) retrieve relevant industry benchmark data from a vector database; (4) generate narrative analysis comparing the company’s performance to benchmarks; and (5) format the final report in executive summary style with specific sections for highlights, concerns, and outlook. Each step has a dedicated prompt with explicit instructions and expected output format.

Intermediate Outputs

Intermediate outputs are the structured or semi-structured data artifacts produced at each step of a prompt chain, which serve as inputs to subsequent steps ²⁴. These outputs are typically formatted as JSON objects, lists, key-value pairs, or other parseable structures rather than free-form text, enabling programmatic validation and routing.

Example: A content moderation system processing user-generated reviews uses intermediate outputs at each stage. The first prompt analyzes a review and outputs JSON with fields: {"sentiment": "negative", "toxicity_score": 0.73, "policy_violations": ["profanity", "personal_attack"], "flagged_phrases": ["terrible service", "incompetent staff"]}. This structured output is then passed to a second prompt that, based on the toxicity_score exceeding 0.7, generates a specific moderation action recommendation. The JSON format allows the orchestration layer to implement conditional logic (if toxicity > 0.7, escalate to human review) that would be impossible with unstructured text.

Sequential Chaining

Sequential chaining is a linear arrangement of prompts where each step depends strictly on the output of the previous step, forming a straightforward pipeline ¹². This is the simplest form of prompt chaining and is appropriate when the task has a clear, unambiguous progression.

Example: A climate research assistant implements sequential chaining to analyze temperature data. Step 1 receives raw temperature readings and outputs: “Global average temperatures have increased 1.2°C since pre-industrial times, with accelerated warming in polar regions.” Step 2 takes this summary and searches a scientific database, returning: “Found 47 relevant studies on polar amplification and feedback mechanisms.” Step 3 summarizes key findings from those studies: “Research indicates ice-albedo feedback and methane release from permafrost are primary drivers.” Step 4 uses all previous outputs to propose: “Mitigation strategies should prioritize Arctic monitoring systems and methane capture technologies” ². Each step builds directly on the previous one without branching.

Conditional Chaining

Conditional chaining incorporates if-then logic and branching, routing outputs to different subsequent prompts based on classifications, thresholds, or other criteria evaluated at each step ¹⁸. This enables chains to adapt their behavior based on intermediate results.

Example: A customer service automation system uses conditional chaining to handle support tickets. After the first prompt classifies an incoming ticket, the chain branches: if category == "billing_dispute" AND amount > $500, route to a specialized prompt that gathers transaction details and generates a formal dispute resolution response requiring manager approval; if category == "technical_support" AND complexity_score < 3, route to a prompt that searches the knowledge base and generates a self-service solution; if sentiment == "angry" regardless of category, route to an empathy-focused prompt that prioritizes de-escalation language before addressing the issue. Each branch uses different prompt templates optimized for that specific scenario.

Loop Chaining

Loop chaining applies the same chain or chain segment repeatedly, either across multiple inputs (batch processing) or iteratively refining a single artifact until quality criteria are met ¹². This pattern is essential for scaling chains to large datasets or implementing self-improvement workflows.

Example: A legal document review system uses loop chaining to process 500 contracts for compliance. The chain (extract clauses → classify risk level → flag non-standard terms → generate summary) runs once per contract, with outputs aggregated into a master spreadsheet. Separately, a content generation system uses iterative loop chaining: it generates a draft marketing email, then enters a refinement loop where a critique prompt evaluates tone, clarity, and call-to-action strength (outputting scores 1-10), and if any score is below 7, a revision prompt rewrites the draft addressing the specific weaknesses. This loop repeats up to three times or until all scores exceed 7, ensuring consistent quality.

Validation and Guardrails

Validation and guardrails are automated checks applied to intermediate outputs to catch errors, policy violations, or hallucinations before they propagate through the chain ⁴⁵⁶. These can include schema validation, regex patterns, business rules, or separate LLM-based critic prompts.

Example: A medical information system extracting patient data from clinical notes implements multiple guardrails. After the extraction prompt outputs JSON with patient demographics, diagnoses, and medications, a validation layer checks: (1) date fields match ISO format and are not in the future; (2) medication names exist in an approved formulary database; (3) diagnosis codes are valid ICD-10 entries. If validation fails, the system either rejects the output and retries with an enhanced prompt that includes the validation error, or routes to human review. Additionally, a separate “safety critic” prompt reviews the extracted information and outputs a confidence score; if confidence < 0.85, the record is flagged for clinician verification before entering the electronic health record.

Orchestration

Orchestration refers to the code, framework, or platform that executes the prompt chain, manages data flow between steps, implements control logic, and handles monitoring, logging, and error recovery ⁴⁵. Orchestration transforms a conceptual chain design into a functioning system.

Example: A data analytics company builds orchestration for a market research chain using Python and a workflow framework. The orchestration layer: (1) reads input queries from a queue; (2) calls the LLM API for each prompt step with appropriate temperature and token settings; (3) parses JSON responses and validates schemas; (4) implements retry logic with exponential backoff if API calls fail; (5) logs each step’s input, output, latency, and token count to a monitoring database; (6) implements conditional branching by evaluating output fields and routing to different prompt templates; (7) aggregates final results and writes to a data warehouse. The orchestration code treats each prompt as a versioned configuration file, enabling A/B testing of prompt variations and rollback if quality degrades ⁵.

Applications in LLM System Design

Retrieval-Augmented Generation (RAG) Pipelines

Prompt chaining is fundamental to RAG systems, which combine LLM generation with external knowledge retrieval ⁴⁶. A typical RAG chain implements multiple distinct stages: query analysis and expansion, embedding-based retrieval from vector databases, re-ranking of retrieved documents, context synthesis, and final answer generation. For example, a legal research assistant first uses a prompt to reformulate a lawyer’s natural language question into structured search terms and identifies relevant practice areas. A second step retrieves the top 20 case summaries from a vector database using embeddings. A third prompt re-ranks these by relevance and recency, selecting the top 5. A fourth prompt synthesizes key precedents from those cases. Finally, a fifth prompt generates a memo-style answer citing specific cases and statutes. This multi-stage approach dramatically improves answer quality and attribution compared to single-shot generation ⁴⁶.

Multi-Stage Code Generation

Software development workflows benefit from prompt chaining that mirrors the development lifecycle ²⁶. A code generation system might implement: (1) a requirements analysis prompt that converts a feature description into structured specifications with input/output schemas, edge cases, and constraints; (2) a scaffolding prompt that generates function signatures, class structures, and type definitions; (3) an implementation prompt that writes the actual logic for each function; (4) a test generation prompt that creates unit tests covering normal and edge cases; (5) a review prompt that checks for security vulnerabilities, performance issues, and style violations; and (6) a documentation prompt that generates docstrings and usage examples. Each stage produces artifacts that inform the next, and validation steps between stages catch errors early. This approach yields more robust, maintainable code than asking for complete implementations in one shot ⁶⁸.

Content Moderation and Safety Pipelines

Organizations deploying LLMs in customer-facing applications use prompt chains to implement multi-layered safety checks ⁴⁶. A content moderation chain for a social platform might include: (1) a classification prompt that categorizes user-generated content by type (review, question, discussion) and flags potential issues (spam, harassment, misinformation); (2) a toxicity analysis prompt that scores harmful content dimensions and extracts specific problematic phrases; (3) a context evaluation prompt that determines whether flagged content might be acceptable given context (e.g., quoting offensive material to criticize it); (4) a policy mapping prompt that matches violations to specific community guidelines; and (5) an action recommendation prompt that suggests responses (remove, warn, escalate to human review) with justifications. Intermediate outputs are logged for audit trails, and human moderators review only the subset flagged as ambiguous, dramatically improving efficiency while maintaining safety standards ⁴⁶.

Research and Analysis Workflows

Knowledge workers use prompt chains to automate complex research tasks ²⁸. An investment analyst researching a company might deploy a chain that: (1) extracts key financial metrics and business model details from the latest 10-K filing; (2) searches news databases for recent developments and sentiment; (3) retrieves competitor financial data for benchmarking; (4) identifies relevant industry trends from analyst reports; (5) synthesizes findings into a SWOT analysis; and (6) generates investment thesis scenarios (bull case, base case, bear case) with supporting evidence. Each step produces structured outputs that feed forward, and the analyst can inspect intermediate results to verify accuracy and adjust the chain’s direction. This transforms a multi-day research process into a supervised workflow that completes in hours ²⁶⁸.

Best Practices

Start Simple and Decompose Progressively

Begin with the simplest possible chain that addresses the core task, then add steps only when they yield measurable improvements in accuracy, reliability, or safety ⁴⁶. Premature complexity increases latency, cost, and debugging difficulty without guaranteed benefits.

Rationale: Each additional step in a chain introduces overhead (API latency, token costs, potential failure points) and makes the system harder to understand and maintain. Starting simple establishes a performance baseline and helps identify which subtasks actually benefit from decomposition.

Implementation Example: A summarization system initially uses a single prompt to condense long documents. After evaluation, the team discovers that summaries of technical documents miss key details. They add a two-step chain: first, extract technical terms and their definitions; second, generate a summary that preserves those terms. This targeted decomposition improves technical accuracy by 23% without over-engineering. They resist adding further steps (sentiment analysis, readability scoring) until specific quality issues emerge that warrant them ⁶.

Structure Intermediate Outputs with Explicit Schemas

Design each prompt to produce outputs in well-defined formats (JSON, YAML, structured lists) with explicit field names, types, and constraints ²⁴⁶. Include output format specifications and examples directly in prompt instructions.

Rationale: Structured outputs enable programmatic validation, conditional routing, and reliable parsing by subsequent steps. They also make chains more debuggable by providing clear contracts between steps, and they reduce ambiguity that can lead to hallucinations or format errors.

Implementation Example: A data extraction chain includes this instruction in each prompt: “Output your response as valid JSON with exactly these fields: {\"entities\": [list of strings], \"dates\": [list in YYYY-MM-DD format], \"amounts\": [list of numbers], \"confidence\": float between 0 and 1}. Do not include any text outside the JSON object.” The orchestration layer parses this JSON, validates that all required fields exist and match expected types, and rejects outputs that fail validation. This explicit schema reduces parsing errors from 18% to under 2% compared to free-form outputs ²⁴.

Implement Validation and Self-Correction at Critical Steps

Insert validation checks after steps that are error-prone or safety-critical, and use self-correction loops where the model critiques and revises its own outputs ⁴⁶. This catches mistakes before they propagate and compound through the chain.

Rationale: Errors early in a chain can cascade, causing all subsequent steps to produce flawed outputs. Validation and self-correction create checkpoints that isolate failures and improve overall reliability without requiring perfect performance at every step.

Implementation Example: A financial report generation chain includes a validation step after numerical calculations. The system prompts: “Review these calculated growth rates: [data]. Check for: (1) mathematical accuracy, (2) logical consistency (e.g., revenue growth should align with unit growth × price changes), (3) outliers that seem implausible. Output: {\"valid\": true/false, \"issues\": [list of problems], \"corrected_values\": {field: new_value}}.” If valid == false, the orchestration layer either applies the corrections automatically or routes to human review depending on the severity of issues flagged. This validation step reduces downstream errors in executive summaries by 34% ⁴⁶.

Log Comprehensively and Monitor Step-Level Metrics

Instrument each step to log inputs, outputs, latency, token counts, model versions, and any validation results ⁴⁵. Aggregate these logs to identify bottlenecks, quality issues, and opportunities for optimization.

Rationale: Chains are complex systems where overall performance depends on every step. Step-level observability enables precise debugging (identifying exactly which prompt caused a failure), cost optimization (finding expensive steps to refactor), and continuous improvement (A/B testing prompt variations at specific steps).

Implementation Example: A customer support chain logs to a structured database: {step_id, timestamp, input_tokens, output_tokens, latency_ms, model, temperature, prompt_version, output_valid, confidence_score, user_id, session_id}. A monitoring dashboard shows that Step 3 (knowledge base search) accounts for 60% of total latency and has a validation failure rate of 8%. The team optimizes by caching frequent searches and revising the prompt to improve output formatting, reducing latency by 40% and failures to 2%. Without step-level metrics, these issues would have been invisible in aggregate performance numbers ⁴⁵.

Implementation Considerations

Tool and Framework Selection

Implementing prompt chains requires choosing appropriate tools for orchestration, API management, and monitoring ⁴⁵⁶. Options range from simple scripting with direct API calls to sophisticated workflow frameworks and LLM-specific orchestration platforms.

Considerations: For prototyping and simple chains, direct API calls in Python or JavaScript with basic error handling may suffice. As chains grow more complex, teams often adopt workflow frameworks that provide built-in retry logic, parallel execution, state management, and monitoring. Enterprise deployments may require platforms that integrate with existing data infrastructure, support governance and audit requirements, and provide observability dashboards ⁴⁵.

Example: A startup building a document analysis product initially implements chains as Python scripts with sequential API calls and JSON file storage for intermediate outputs. As they scale to thousands of documents daily, they migrate to a workflow orchestration framework that provides: parallel processing of independent steps, automatic retry with exponential backoff, persistent state storage, and integration with their vector database and monitoring stack. This migration reduces processing time by 70% through parallelization and improves reliability by handling transient API failures automatically ⁵.

Context Window Management and Information Flow

Chains must carefully manage what information flows between steps, balancing completeness against context window limits ¹⁴⁶. Passing full text forward can quickly exhaust token budgets, while passing only summaries risks losing critical details.

Considerations: Design intermediate outputs to be information-dense and structured. Use retrieval (embeddings, vector databases) to selectively reintroduce context at steps that need it rather than carrying everything forward. Consider which steps truly need full context versus which can work with summaries or extracted facts ⁴⁶.

Example: A legal contract analysis chain processes 50-page agreements. Rather than passing the full contract text to every step (which would exceed context limits by Step 3), the chain: (1) extracts structured data (parties, dates, key terms, obligations) into JSON; (2) generates a 500-word summary; (3) creates embeddings of each contract section and stores them in a vector database. Subsequent steps receive the JSON and summary, but can query the vector database to retrieve specific sections when needed (e.g., “retrieve clauses related to indemnification”). This approach keeps context usage under 4,000 tokens per step while maintaining access to full contract details ⁴⁶.

Cost and Latency Optimization

Each step in a chain incurs API costs and latency, which can accumulate significantly in multi-step workflows ⁴⁵. Optimization requires balancing thoroughness against efficiency.

Considerations: Profile chains to identify expensive steps (high token counts, slow models). Consider whether some steps can use smaller/faster models without sacrificing quality. Implement caching for repeated operations. Use parallel execution where steps are independent. Evaluate whether certain steps are necessary or can be combined ⁴⁵.

Example: A content generation chain initially uses GPT-4 for all five steps, costing $0.12 per execution and taking 18 seconds. Analysis reveals that Steps 1 (classification) and 2 (extraction) are straightforward and succeed 98% of the time with GPT-3.5, which is 10× cheaper and 3× faster. Steps 3-5 (analysis, generation, refinement) genuinely benefit from GPT-4’s capabilities. By using GPT-3.5 for Steps 1-2 and GPT-4 for Steps 3-5, the team reduces cost to $0.06 per execution and latency to 12 seconds while maintaining output quality. They also implement caching for Step 1 classifications of common input types, further reducing costs by 15% ⁴⁵.

Versioning and Testing

Prompt chains are complex systems where changes to any step can affect overall behavior ⁴⁶. Rigorous versioning and testing practices are essential for maintaining reliability.

Considerations: Treat prompts as versioned artifacts with change tracking. Maintain regression test suites with representative inputs and expected outputs for each step and for end-to-end chains. Implement staged rollouts (dev → staging → production) with automated quality checks. Use A/B testing to compare prompt variations ⁴⁶.

Example: A data pipeline team maintains prompts in a Git repository with semantic versioning (e.g., extraction_prompt_v2.3.1). Each prompt version includes: the prompt text, model parameters (temperature, max_tokens), example inputs/outputs, and performance benchmarks. Before deploying changes, they run a regression suite of 200 test cases covering normal inputs, edge cases, and known failure modes. A prompt change that improves accuracy on one test category but degrades another triggers review. In production, they deploy new prompt versions to 10% of traffic first, monitoring step-level quality metrics for 24 hours before full rollout. This process has prevented three incidents where prompt changes would have degraded production quality ⁴⁶.

Common Challenges and Solutions

Challenge: Error Propagation and Compounding

Mistakes made early in a prompt chain can propagate through subsequent steps, causing cascading failures where each step builds on flawed inputs from the previous step ⁴⁶. For example, if an extraction step misidentifies a date as “2023-13-45” (an invalid date), subsequent steps that calculate time periods or filter by date ranges will produce nonsensical results. These compounding errors are particularly problematic because the final output may appear superficially plausible while being fundamentally incorrect.

Solution:

Implement validation checkpoints after error-prone steps, using a combination of programmatic checks and LLM-based critics ⁴⁶. For structured outputs, validate against schemas (e.g., dates must match YYYY-MM-DD format and represent valid calendar dates, numerical values must fall within expected ranges). For semantic correctness, use a separate “critic” prompt that reviews the output and flags inconsistencies. When validation fails, implement retry logic with enhanced prompts that include the validation error: “Previous attempt produced invalid date ‘2023-13-45’. Dates must be in YYYY-MM-DD format with valid month (01-12) and day. Please re-extract dates from the text.” For critical applications, implement redundancy where two independent prompts perform the same extraction and a third reconciles differences. Finally, set confidence thresholds: if a step outputs low confidence scores, route to human review rather than continuing the chain. A financial services company using these techniques reduced cascading errors in their document processing chain from 12% to under 1% ⁴⁶.

Challenge: Context Window Limitations and Information Loss

As chains progress through multiple steps, the cumulative context (original input plus all intermediate outputs) can exceed model context windows, forcing difficult choices about what information to retain ¹⁴. Simply passing summaries forward risks losing critical details, while attempting to pass everything forward quickly becomes infeasible. This is especially problematic for chains processing long documents or accumulating information across many steps.

Solution:

Adopt a hybrid approach combining structured extraction, selective summarization, and retrieval-augmented context ⁴⁶. At each step, extract key facts into structured formats (JSON, key-value pairs) that are information-dense and easy to parse. Generate concise summaries that preserve essential context while discarding verbosity. Store full intermediate outputs in a vector database with embeddings, allowing later steps to retrieve specific information on-demand rather than carrying everything forward. Design prompts to explicitly specify what information they need, then fetch only that context. For example, a contract analysis chain might extract structured terms (parties, dates, amounts) and generate a 300-word summary to pass forward, while storing full clause text in a vector database. When Step 5 needs to analyze indemnification clauses specifically, it queries the database for relevant sections rather than having received the entire contract at every step. This approach keeps per-step context under 4,000 tokens while maintaining access to complete information, enabling chains with 10+ steps on documents exceeding 50 pages ⁴⁶.

Challenge: Debugging and Failure Localization

When a prompt chain produces incorrect or unexpected final outputs, identifying which specific step caused the problem can be difficult, especially in chains with 5+ steps and conditional branching ⁴⁵. Without proper instrumentation, developers face a “black box” where they know the final output is wrong but cannot pinpoint whether the issue lies in extraction, transformation, reasoning, or formatting.

Solution:

Implement comprehensive step-level logging and observability from the outset ⁴⁵. Log each step’s complete input, output, model parameters, latency, token counts, and any validation results to a structured database or logging platform. Include unique identifiers that link all steps in a single chain execution. Build dashboards that visualize chain execution flows, highlighting steps with validation failures, high latency, or low confidence scores. When investigating failures, trace backward through the logs to identify exactly where incorrect information was introduced. Implement “replay” functionality that can re-execute individual steps with logged inputs to test prompt modifications. Use automated analysis to identify patterns: if Step 3 has a 15% validation failure rate while other steps are under 2%, focus optimization efforts there. One development team reduced debugging time from hours to minutes by implementing structured logging with a visualization dashboard that color-codes step status (green=success, yellow=low confidence, red=validation failure) and allows clicking any step to view full input/output details ⁴⁵.

Challenge: Balancing Chain Complexity with Latency and Cost

Each additional step in a chain increases total latency (due to sequential API calls) and cost (due to additional token consumption), creating tension between thoroughness and efficiency ⁴⁵. A chain with 8 steps might produce higher quality outputs than a 3-step chain, but if it takes 30 seconds and costs $0.50 per execution versus 8 seconds and $0.10, the trade-off may not be worthwhile for all use cases.

Solution:

Profile chains systematically to understand the cost/latency/quality trade-offs at each step, then optimize strategically ⁴⁵. Measure step-level latency and token consumption to identify bottlenecks. Evaluate whether expensive steps can use smaller, faster models without quality degradation—often classification, extraction, and formatting steps work well with less capable models, while only complex reasoning and generation require premium models. Implement parallel execution for independent steps (e.g., if Steps 3 and 4 both depend only on Step 2, run them concurrently). Cache results for repeated operations (e.g., if many chains classify similar inputs, cache classification results). Consider conditional execution where expensive steps only run when needed (e.g., detailed analysis only for high-value transactions). Evaluate whether some steps can be combined without losing modularity. A content moderation system reduced latency from 12 seconds to 5 seconds by: (1) switching Steps 1-2 to a faster model (saving 3 seconds), (2) running Steps 4-5 in parallel (saving 2 seconds), and (3) caching common classification results (saving 2 seconds on 40% of requests), while maintaining the same quality metrics ⁴⁵.

Challenge: Maintaining Consistency and Coherence Across Steps

In multi-step chains, outputs from different steps may contradict each other or exhibit inconsistent tone, style, or level of detail, especially when steps use different prompts or models ²⁶. For example, an early step might extract “Q3 revenue: $45M” while a later calculation step reports “$47M revenue,” or a professional tone in analysis steps might clash with casual language in the final summary.

Solution:

Establish explicit consistency requirements and implement reconciliation mechanisms ²⁶. Define a “source of truth” principle where certain steps are authoritative for specific facts (e.g., extraction steps are authoritative for numerical data, which later steps must reference rather than recalculate). Include consistency instructions in prompts: “Use exactly the figures provided in the input JSON; do not recalculate or round.” Implement a reconciliation step that checks for contradictions between intermediate outputs and flags inconsistencies for resolution. For tone and style, create a style guide and include it in relevant prompts, or use a final “harmonization” step that reviews the complete output for consistency. Use the same model and temperature settings across steps where consistency matters. A report generation chain reduced inconsistencies from 23% to 4% by: (1) making the extraction step the single source of truth for all numerical data, (2) including “Use these exact figures: [JSON]” in all subsequent prompts, (3) adding a final review step that checks whether numbers in the narrative match the source JSON, and (4) using consistent tone instructions (“professional but accessible, avoid jargon”) across all generation steps ²⁶.

See Also

• Retrieval-Augmented Generation (RAG)
• Prompt Engineering Best Practices
• Zero-Shot Prompting

References

1. TechTarget. (2024). Prompt chaining. https://www.techtarget.com/searchenterpriseai/definition/prompt-chaining
2. DataCamp. (2024). Prompt Chaining for LLMs. https://www.datacamp.com/tutorial/prompt-chaining-llm
3. Shieldbase. (2024). Prompt Chaining vs Chain of Thought Prompting. https://shieldbase.ai/blog/prompt-chaining-vs-chain-of-thought-prompting
4. Nexla. (2024). Prompt Chaining. https://nexla.com/enterprise-ai/prompt-chaining/
5. CodeSignal. (2024). Breaking Down Tasks with Prompt Chaining. https://codesignal.com/learn/courses/exploring-workflows-with-claude/lessons/breaking-down-tasks-with-prompt-chaining-1
6. Prompt Engineering Guide. (2024). Prompt Chaining. https://www.promptingguide.ai/techniques/prompt_chaining
7. Wordware. (2024). The Secret Power of Prompt Chaining. https://blog.wordware.ai/the-secret-power-of-prompt-chaining
8. PromptHub. (2024). Prompt Chaining Guide. https://www.prompthub.us/blog/prompt-chaining-guide