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Summary – Faced with hallucination risks, runaway costs, and lack of contextualization, AI projects struggle to reach production scale. LangChain structures development into modular blocks (chains, agents, business data injection), RAG ensures precision and token optimization, while LangServe, LangSmith, or a Python backend provide the right level of governance.
Solution: launch a proof-of-concept with LangChain+RAG, then scale to an orchestrated platform to master costs, performance, and business compliance.
Applications based on large language models (LLMs) are both promising and challenging to implement. Hallucinations, costs linked to poorly optimized prompts, and the difficulty of leveraging precise business data hamper their large-scale adoption. Yet, Swiss companies, from banks to industrial players, are looking to automate analysis, text generation, and decision support with AI. Integrating a framework like LangChain, coupled with the retrieval-augmented generation (RAG) method, optimizes response accuracy, controls costs, and maintains strict oversight of business context. This article details the best practices for building a reliable, high-performing, and cost-efficient AI application. In this article, we will explore the concrete challenges specific to LLM development, why LangChain and RAG provide solutions, and finally how to deploy your AI solution using these technologies.
Concrete challenges in AI development with LLMs
LLMs are prone to hallucinations and sometimes produce vague or erroneous responses.The lack of cost control over API calls and the injection of business data jeopardizes the viability of an AI project.
Hallucinations and factual consistency
Language models sometimes generate unverified information, risking the spread of errors or recommendations that have never been validated. This inaccuracy can undermine user trust, especially in regulated contexts such as finance or healthcare.
To mitigate these deviations, it is essential to associate every generated response with documentary evidence or a reliable source. Without a validation mechanism, each hallucination can become a strategic vulnerability.
For example, a private bank first deployed a prototype AI chatbot to assist its advisors. Quickly, inaccurate answers about financial products alerted the project team. Implementing a mechanism to retrieve internal documents reduced these discrepancies by 80%.
High costs and prompt optimization
Every call to an LLM API incurs a cost based on the number of tokens sent and received. Poorly structured or overly verbose prompts can quickly drive expenses to several thousand francs per month. To learn more about optimizing total cost of ownership.
Optimization involves breaking down the query, limiting the context transmitted, and using lighter models for less critical tasks. This modular approach reduces expenditure while maintaining an appropriate quality level. For practical tips on reducing operational costs.
A B2B service company, for instance, saw its GPT-4 cloud bill increase by 200%. After revising its prompts and segmenting its call flow, it cut costs by 45% without sacrificing client satisfaction.
Injection of precise business data
LLMs do not know your internal processes or regulatory frameworks. Without targeted data injection, they rely on general knowledge that may be outdated or unsuitable.
Ensuring accuracy requires linking each query to the correct documents, databases, or internal APIs. Yet this integration often proves costly and complex.
A Zurich-based industrial leader deployed an AI assistant to address technical questions from its teams. Adding a module to index PDF manuals and internal databases halved the error rate in usage advice.
Why LangChain makes a difference for creating an AI application
LangChain structures AI application development around clear, modular components.It simplifies building intelligent workflows, from simple prompts to executing actions via APIs, while remaining open source and extensible.
Modular components for every building block
The framework offers abstractions for model I/O, data retrieval, chain composition, and agent coordination. Each component can be selected, developed, or replaced without impacting the rest of the system.
This modularity is a major advantage for avoiding vendor lock-in. Teams can start with a simple Python backend and migrate to more robust solutions as needs evolve.
For example, a logistics company in Lausanne used LangChain to prototype a shipment tracking chatbot. The Stripe retrieval modules and internal API calls were integrated without touching the core Text-Davinci engine, ensuring a rapid proof of concept.
Intelligent workflows and chains
LangChain enables the composition of multiple processing steps: text cleaning, query generation, context enrichment, and post-processing. Each step is defined and testable independently, ensuring overall workflow quality.
The “chain of thought” approach helps decompose a complex question into sub-questions, improving response relevance. Chain transparency also simplifies debugging and auditing.
A Geneva-based pharmaceutical company implemented a LangChain chain to analyze customer feedback on a new medical device. Decomposing queries into steps increased semantic analysis accuracy by 30%.
AI agents and tools for action
LangChain agents orchestrate multiple models and external tools, such as business APIs or Python scripts. They go beyond simple text generation to perform automated actions securely.
Whether calling an ERP, fetching a status report, or triggering an alert, the agent maintains coherent context and logs every action, ensuring compliance and post-operation review.
LangChain is thus a powerful tool for integrating AI agents into your ecosystem and elevating process automation to the next level.
An Jura-based watchmaking company, for example, automated production report synthesis. A LangChain agent retrieves factory data, generates a summary, and automatically sends it to managers, reducing reporting time by 75%.
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RAG: the indispensable ally for efficient LLM apps
Retrieval-augmented generation enriches responses with specific, up-to-date data from your repositories.This method reduces the number of tokens used, lowers costs, and improves quality without altering the base model.
Enriching with targeted data
RAG adds a document retrieval layer before generation. Relevant passages are extracted and injected into the prompt, ensuring the response is based on concrete information rather than the model’s general knowledge.
The process can target SQL databases, indexed PDF documents, or internal APIs, depending on the use case. The result is a contextualized, verifiable answer.
A typical example is a Bern legal firm that implemented RAG for its internal search engine. Relevant contract clauses are extracted before each query, ensuring accuracy and reducing third-party requests by 60%.
Token reduction and cost control
By limiting the prompt to the bare essentials and letting the retrieval phase do the heavy lifting, you significantly reduce the number of tokens sent. This, in turn, lowers the cost per query.
Companies can choose a lighter model for generation while leveraging the rich context provided by RAG. This hybrid strategy combines performance and savings.
A Zurich financial services provider, for instance, achieved a 40% reduction in OpenAI consumption after switching its pipeline to a smaller model paired with a RAG process for compliance report generation.
Quality and relevance without altering the language model
RAG improves performance while remaining non-intrusive: the original model is not retrained, avoiding costly cycles and lengthy training phases. Flexibility remains maximal.
You can finely tune data freshness (real-time, weekly, monthly) and add business filters to restrict sources to validated repositories.
A Geneva-based holding company implemented RAG to power its financial analysis dashboard. Defining time windows for extracts enabled delivering up-to-date recommendations daily.
Deploying an AI application: LangServe, LangSmith, or a custom backend?
The choice between LangServe, LangSmith, or a classic Python backend depends on the desired level of control and project maturity.Starting small with a custom server ensures flexibility and rapid deployment, while a structured platform eases scaling and monitoring.
LangServe vs. a classic Python backend
LangServe offers a ready-to-use server for your LangChain chains, simplifying hosting and updates. In contrast, a custom Python backend remains pure open source with no proprietary layer.
For a quick POC or pilot project, the custom backend can be deployed in hours. The code stays 100% controlled, versioned, and extensible according to your specific needs.
LangSmith for testing and monitoring
LangSmith complements LangChain by providing a testing environment, request tracing, and performance metrics. It simplifies debugging and collaboration between data, dev, and business teams.
The platform allows you to replay a request, inspect each chain step, and compare different prompts or models. It accelerates quality assurance for critical projects.
Scaling to a structured platform
As usage intensifies, moving to a more integrated solution offers better governance: secret management, cost tracking, chain and agent versioning, and proactive alerting.
A hybrid approach remains recommended: keep the open source core while adding an observability and orchestration layer once the project reaches a certain complexity threshold.
Make artificial intelligence your competitive advantage
LangChain combined with RAG provides a robust foundation for building reliable, fast, and cost-effective AI applications. This method ensures response consistency, cost control, and secure integration of your proprietary business expertise.
Whether you are starting a proof of concept or planning large-scale industrialization, at Edana our experts support your project from initial architecture to production deployment, adapting each component to your context.
