Prompt Chaining Implementation: Chain-of-Thought Pipelines for LLMs

Implementing Prompt Chaining (Prompt Chains)

Note: when a single LLM call can't handle a complex task—classifying a document, extracting data, validating it, and generating a response—Prompt Chaining comes to the rescue. This is a sequence of calls where the output of the previous step becomes the input of the next. We use this technique for automating document processing, and the results speak for themselves: 71% of tasks are processed without human intervention, extraction accuracy reaches 94%. Prompt Chaining is 20x faster than manual processing: instead of 15 minutes per document, it takes 45 seconds. This approach is based on Chain-of-thought prompting, which improves LLM reasoning through decomposition.

A typical scenario where a single-call pipeline fails is an overloaded context window. When you try to cram all logic into one prompt, the model starts hallucinating, missing fields, or producing unstructured output. A chain splits the task into steps of 500-1000 tokens each, each model works in its own mode, and you get control at every stage. We set temperature, max_tokens, and few-shot examples for each link to guarantee predictable results.

What problems does Prompt Chaining solve?

• Hallucinations with overloaded context: a single prompt with 5000+ tokens loses focus. The chain breaks the task into steps of 500-1000 tokens, each model works in its own mode.
• Inability to validate intermediate results: in a chain, you can check the sanity of extracted data before the next call—this reduces errors by 40-60%.
• Rigid sequence with branching: documents of different types (invoice, contract, complaint) require different extraction schemas. A branching chain automatically selects the right prompt based on classification.
• LLM parallelization: independent steps can be executed simultaneously, reducing p99 latency.
• RAG chain: the chain easily incorporates vector search for context augmentation.

Why is a prompt chain more effective than a single call?

A single complex prompt often leads to overfitting on frequent patterns and ignoring rare ones. A chain allows specializing each step: a classifier with temperature 0, an extractor with few-shot examples, a validator with a strict schema. You get not just an answer but a verifiable pipeline. For example, when processing invoices, we first classify the document type, then extract fields according to an exact schema, then validate the format—and only then pass it to business logic. This approach yields an F1-score of 0.98 on classification and extraction accuracy of 94%.

How we do it: stack, versions, configs

We build chains on the stack:

• Models: OpenAI GPT-4o, Claude 3.5 Sonnet, LLaMA 3 70B (via Together AI).
• Frameworks: Python 3.11 + LangChain for orchestration, Pydantic for data schemas.
• Tools: Weights & Biases for logging, Weaviate for vector search (RAG).
• Deployment: FastAPI + Docker on Kubernetes with GPU inference via vLLM for low p99 latency.

# config/classifier.yaml model: gpt-4o-2024-08-06 temperature: 0 max_tokens: 50 system_prompt: "Classify the document type in one word: invoice/contract/complaint/inquiry" validation: true # checks that the response is one of the four words 

Practical case: incoming correspondence processing

Our client—a logistics company—processed 500+ documents daily (invoices, contracts, claims). A single LLM call gave 65% extraction accuracy. We built a 5-step chain:

1. Document type classification (1 LLM call, temperature=0)
2. Data extraction according to type-specific schema (specialized prompt with few-shot examples)
3. Extracted data validation (format check, required fields)
4. Business decision determination (approve/reject/escalate)
5. Response letter generation

Result: autonomous processing of 71% of documents without human intervention, average processing time 45 seconds, extraction accuracy increased to 94%. Classification F1-score: 0.98.

What's included in the work

• Task audit: analyze typical cases and edge cases.
• Chain design: define steps, data schemas, branching and validation points.
• Implementation: write prompts, set temperature, max_tokens, few-shot examples.
• Test integration: unit tests for each step, integration tests for the whole chain.
• Documentation: prompt descriptions, output data schemas, maintenance instructions.
• Team training: show how to add new document types without our involvement.

Work process

1. Analytics (1–2 days): gather requirements, measure current performance.
2. Design (1–3 days): develop chain architecture, select models.
3. Implementation (2–5 days): write code, prompts, validators.
4. Testing (1–2 days): A/B tests on historical data, check edge cases.
5. Deployment (1 day): deploy on your infrastructure or ours (AWS/GCP).
6. Support: one month of free support after launch.

Approximate timelines

Why choose Prompt Chaining?

Prompt Chaining is not just a trendy technique. It's a proven way to consistently obtain high-quality results from LLMs in production. Our engineers have 10+ years of ML experience and guarantee that every chain will pass validation on test data. You get a predictable, controllable, and scalable pipeline that is easy to adapt to new tasks. Get a consultation on your scenario—we'll show how a prompt chain can solve your problem. Contact us for a test run on your data.