In today’s digital land­scape, Large Lan­guage Mod­els (LLMs) have emerged as a power­ful tool for gen­er­at­ing text, answer­ing ques­tions, and assist­ing users across vari­ous applic­a­tions. How­ever, the vast amount of data LLMs are trained on can some­times raise con­cerns about the accur­acy and reli­ab­il­ity of their responses, par­tic­u­larly when trust in the source of inform­a­tion is para­mount. Ima­gine a cus­tomer ser­vice chat­bot provid­ing answers based on unveri­fied inter­net sources or a tech­nical sup­port bot that may not have access to the latest manu­als and instruc­tion doc­u­ments. In such scen­arios, trust in the source of inform­a­tion becomes crucial.

The Chal­lenge: Trust and Reliability

LLMs are trained on large amounts of data from the inter­net, encom­passing diverse sources and view­points. While this makes them ver­sat­ile, it also intro­duces the chal­lenge of ensur­ing that their responses are based on reli­able, veri­fied inform­a­tion. Work­ing with out-of-the box LLMs imposes vari­ous challenges:

Hal­lu­cin­a­tions in LLMs: Hal­lu­cin­a­tions refer to the gen­er­a­tion of text that appears plaus­ible but is entirely fic­tional or fac­tu­ally incor­rect. These hal­lu­cin­a­tions occur due to the stat­ist­ical nature of LLMs, which are trained to pre­dict the most likely next word or phrase based on pat­terns in their train­ing data. Con­sequently, LLMs may gen­er­ate con­tent that aligns with their train­ing data but lacks real-world accur­acy, poten­tially lead­ing to the spread of mis­in­form­a­tion and under­min­ing trust in their output.

Lack of Con­tex­tual Under­stand­ing: LLMs are incred­ibly tal­en­ted, but they lack con­text about spe­cific organ­iz­a­tions, indus­tries, or domains. This defi­ciency makes it dif­fi­cult for them to provide accur­ate and con­text-aware responses. LLMs may gen­er­ate gen­eric or irrel­ev­ant inform­a­tion, lead­ing to sub­op­timal res­ults in tasks such as cus­tomer support.

Incon­sist­ent Inform­a­tion: LLMs often pro­duce incon­sist­ent or con­tra­dict­ory responses due to the vast and diverse pat­terns in their train­ing data. Users may find it chal­len­ging to trust LLM-gen­er­ated inform­a­tion when faced with such incon­sist­en­cies, poten­tially lead­ing to decision-mak­ing problems.

Pri­vacy and Secur­ity Con­cerns: Fine-tun­ing LLMs using ser­vice pro­viders may neces­sit­ate shar­ing sens­it­ive or con­fid­en­tial inform­a­tion with the model. This raises legit­im­ate pri­vacy and secur­ity con­cerns, and organ­iz­a­tions must exer­cise cau­tion to avoid expos­ing pro­pri­et­ary data or com­prom­ising user privacy.

Lim­ited Domain Expert­ise: LLMs are gen­eral-pur­pose mod­els and do not pos­sess spe­cial­ized domain expert­ise. In indus­tries or sec­tors that require spe­cific know­ledge or expert­ise, rely­ing solely on LLMs may res­ult in inac­cur­ate or incom­plete information.

The Solu­tion: Retrieval Aug­men­ted Gen­er­a­tion (RAG)

Enter the RAG archi­tec­ture – a solu­tion designed to tackle the trust and reli­ab­il­ity issues asso­ci­ated with LLMs. The primary goal of this archi­tec­ture is to limit the LLM’s responses to inform­a­tion from a pre­defined, trus­ted know­ledge base, ensur­ing that it answers ques­tions using only the provided inform­a­tion. The approach con­sists of two steps: First the rel­ev­ant doc­u­ments or data­bases are selec­ted. Second, the con­text is provided to the LLM to answer the ori­ginal ques­tions. This approach not only empowers the sys­tem to offer answers but also sup­plies the source of inform­a­tion, effect­ively mit­ig­at­ing the risks asso­ci­ated with hal­lu­cin­a­tions and misinformation.

Applic­a­tion Examples: LLMs in Action

At our cli­ents we imple­men­ted a wide range of use-cases. One of the big advant­ages is that the sys­tem provides not only the answer but also the source for fur­ther read­ing and validation.

1. Cus­tomer Ser­vice Chat­bot: A chat­bot that draws its responses exclus­ively from FAQ doc­u­ments cre­ated by the com­pany. Cus­tom­ers can be con­fid­ent that the inform­a­tion they receive is not only accur­ate but also comes from the organ­iz­a­tion itself, instilling trust and reli­ab­il­ity in the interaction.
2. Tech­nical Sup­port Bot: A tech­nical sup­port bot can access manu­als, instruc­tion doc­u­ments, and troubleshoot­ing guides as its exclus­ive know­ledge base. This ensures that cus­tom­ers receive guid­ance based on the latest, veri­fied inform­a­tion, redu­cing the risk of incor­rect advice and enhan­cing trust.
3. Cor­por­ate Doc­u­ment Search: Com­pan­ies can use this archi­tec­ture to cre­ate a power­ful doc­u­ment search tool cap­able of find­ing and sum­mar­iz­ing inform­a­tion from their internal doc­u­ments, reports, and policies. Employ­ees can trust that the res­ults are drawn from author­it­at­ive sources within the organization.
4. Nat­ural Lan­guage Data­base Quer­ies: Sim­ilar to doc­u­ments whole data­base can be made access­ible as a cus­tom know­ledge base. The LLM can trans­late nat­ural lan­guage quer­ies to SQL or sim­ilar data­base lan­guages to retrieve and sum­mar­ize the inform­a­tion. This can be used to gen­er­ate cus­tom reports and, when com­bined with other tech­no­lo­gies, even cre­at­ing dynamic charts on the fly.

Flex­ib­il­ity, Ver­sat­il­ity, and Scalability

The cus­tom know­ledge base archi­tec­ture offers remark­able flex­ib­il­ity, empower­ing organ­iz­a­tions to tailor know­ledge bases to spe­cific needs while seam­lessly integ­rat­ing them into a wide range of applic­a­tions. Unlike LLMs, these know­ledge bases can be swiftly updated to stay cur­rent with evolving inform­a­tion, thus redu­cing the need for resource-intens­ive retrain­ing. These sys­tems can oper­ate around the clock, util­iz­ing any LLM ser­vice pro­vider or on-premise LLM, and can be seam­lessly integ­rated with other sys­tems, such as those designed for per­mis­sion limits.

Fur­ther­more, they provide essen­tial eth­ical safe­guards by enabling organ­iz­a­tions to imple­ment con­trols that effect­ively mit­ig­ate the risk of biased or inap­pro­pri­ate con­tent gen­er­a­tion, thereby ensur­ing respons­ible AI use in today’s dynamic landscape.

Con­clu­sion

In a world increas­ingly reli­ant on AI-powered inter­ac­tions, trust in the source of inform­a­tion is para­mount. The RAG archi­tec­ture offers a power­ful solu­tion to ensure that LLMs provide responses groun­ded in trus­ted and known sources, over­com­ing the lim­it­a­tions of using LLMs in isol­a­tion. Whether it’s enhan­cing cus­tomer ser­vice, deliv­er­ing accur­ate tech­nical sup­port, or enabling effi­cient cor­por­ate doc­u­ment searches, this archi­tec­ture paves the way for more reli­able and trust­worthy AI-driven applic­a­tions, mak­ing tech­no­logy more reli­able, use­ful, and respons­ible while main­tain­ing the integ­rity of inform­a­tion and trust in the digital age.

Out­look and Resources

The frame­works around LLMs with cus­tom know­ledge bases are rap­idly evolving and also the ser­vice pro­viders are teas­ing solu­tions. By the time this blo­g­post is writ­ten, it is prob­ably already out­dated. A few frame­works to look out for are Lang­chain for seam­less integ­ra­tion of vari­ous LLM ser­vices and prompt work­flows. Com­bined with Lla­main­dex this enables the imple­ment­a­tion of a cus­tom know­ledge base architecture.

Here are a few resources to explore:

1. Gen­er­at­ive AI exists because of the trans­former – Excel­lent visual story telling about gen­er­at­ive AI
2. Lang­chain – For seam­less integ­ra­tion of vari­ous LLM ser­vices, prompt work­flows and RAG
3. Lla­main­dex – Frame­work to index doc­u­ments for search & retrieval
4. OpenAI Blog – Intro­du­cing Chat­GPT Enterprise