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Summary – Enterprise AI applications based on LLMs face hallucinations, spiraling API costs and lack of business data control, jeopardizing accuracy and ROI. Leveraging LangChain’s modular chains, agents and RAG-driven retrieval ensures factual consistency, optimized token usage and secure injection of proprietary information while maintaining transparent, testable workflows.
Solution: Deploy a LangChain + RAG architecture with a customizable backend for rapid POCs and scale through LangServe or LangSmith for robust gover
Applications based on large language models (LLMs) are both promising and challenging to implement. Hallucinations, costs associated with inefficient prompts, and the difficulty of leveraging precise business data hamper their large-scale adoption. Yet Swiss companies—from banks to industrial firms—are looking to automate analysis, text generation, and decision support through AI. Integrating a framework like LangChain alongside the RAG (retrieval-augmented generation) method optimizes response relevance, controls costs, and maintains strict oversight of business context. This article details best practices for building a reliable, high-performing, and cost-effective AI app. In this article, we will explore the concrete challenges unique to LLM development, why LangChain and RAG provide solutions, and finally how to deploy an AI solution based on these technologies.
Concrete Challenges in AI Development with LLMs
LLMs are prone to hallucinations and sometimes produce vague or incorrect answers. Lack of control over API costs 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 dissemination 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 drifts, it is essential to link each generated response to a documentary trace or a reliable source. Without a validation mechanism, every hallucination becomes a strategic vulnerability.
For example, a private bank initially deployed an AI chatbot prototype to inform its advisors. Inaccurate responses about financial products quickly alerted the project team. Implementing a mechanism to retrieve internal documents reduced these discrepancies by 80%.
High Costs and Prompt Optimization
Each API call to an LLM incurs a cost based on the number of tokens sent and received. Poorly structured or overly verbose prompts can rapidly drive monthly expenses into the thousands of francs.
Optimization involves breaking down questions, limiting the transmitted context, and using lighter models for less critical tasks. This modular approach reduces expenses while maintaining an appropriate quality level.
A B2B services company, for instance, saw a 200% increase in its GPT-4 cloud bill. After revising its prompts and segmenting its call flow, it cut costs by 45% without sacrificing customer quality.
Injecting Precise Business Data
LLMs do not know your internal processes or regulatory repositories. Without targeted injection, they rely on general knowledge that may be outdated or unsuitable.
Ensuring precision requires linking each query to the right documents, databases, or internal APIs. However, this integration often proves costly and complex.
A Zurich-based industrial leader deployed an AI assistant to answer its teams’ technical questions. Adding a module to index PDF manuals and internal databases halved the error rate in usage advice.
Why LangChain Makes the Difference for Building an AI Application
LangChain structures AI app development around clear, modular components. It simplifies the construction of intelligent workflows—from simple prompts to API-driven actions—while remaining open source and extensible.
Modular Components for Each Building Block
The framework offers abstractions for model I/O, data retrieval, chain composition, and agent coordination. Each component can be chosen, developed, or replaced without impacting the rest of the system.
This modularity helps avoid vendor lock-in. Teams can start with a simple Python backend and migrate to more robust solutions as needs evolve.
A Lausanne logistics company, for example, used LangChain to prototype a shipment-tracking chatbot. 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 composing 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 break down complex questions into sub-questions, improving response relevance. The chain’s transparency also facilitates 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 improved semantic analysis accuracy by 30%.
AI Agents and Action Tools
LangChain agents orchestrate multiple models and external tools, such as business APIs or Python scripts. They go beyond text generation to securely execute automated actions.
Whether calling an ERP, retrieving a system report, or triggering an alert, the agent maintains coherent context and logs each action, ensuring compliance and post-operation review.
LangChain is thus a powerful tool to integrate AI agents within your ecosystem and elevate 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 Essential Ally for Efficient LLM Apps
Retrieval-augmented generation enriches responses with specific, up-to-date data from your repositories. This method reduces token usage, 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 injected into the prompt, ensuring the answer is based on concrete information rather than the model’s general memory.
The process can target SQL databases, indexed PDF documents, or internal APIs, depending on the use case. The result is a contextualized, verifiable response.
A Bernese legal firm, for instance, implemented RAG for its internal search engine. Relevant contractual clauses are extracted before each query, guaranteeing accuracy and reducing third-party requests by 60%.
Token Reduction and Cost Control
By limiting the prompt to the essentials and letting the document retrieval phase handle the heavy lifting, you significantly reduce the number of tokens sent. The cost per request thus drops noticeably.
Companies can choose a lighter model for generation while relying on the rich context provided by RAG. This hybrid strategy marries performance with economy.
A Zurich financial services provider, for example, saved 40% on its OpenAI consumption after switching its pipeline to a smaller model and a RAG-based reporting process.
Quality and Relevance without Altering the Language Model
RAG enhances performance non-intrusively: the original model is not retrained, avoiding costly cycles and long 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 holding company, for instance, used RAG to power its financial analysis dashboard. Defining time windows for extracts enabled up-to-date, day-by-day recommendations.
Deploying an AI Application: LangServe, LangSmith, or 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 speed of deployment, while a structured platform eases scaling and monitoring.
LangServe vs. Classic Python Backend
LangServe provides a ready-to-use server for your LangChain chains, simplifying hosting and updates. A custom Python backend, by contrast, 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 remains fully controlled, versioned, and extensible 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 among data, dev, and business teams.
The platform lets you replay a request, inspect each chain step, and compare different prompts or models. It’s a quality accelerator for critical projects.
Scaling to a Structured Platform
As usage intensifies, moving to a more integrated solution offers better governance: secret management, cost tracking, versioning of chains and agents, proactive alerting.
A hybrid approach is recommended: keep the open-source core while leveraging an observability and orchestration layer once the project reaches a certain complexity threshold.
Make AI Your Competitive Advantage
LangChain combined with RAG provides a robust foundation for building reliable, fast, and cost-effective AI applications. This approach ensures response consistency, cost control, and secure integration of your proprietary business expertise.
Whether you’re launching a proof-of-concept or planning large-scale industrialization, Edana’s experts support your project from initial architecture to production deployment, tailoring each component to your context.
